Patent Publication Number: US-8531542-B2

Title: Techniques for acquiring and processing statistics data in an image signal processor

Description:
BACKGROUND 
     The present disclosure relates generally to digital imaging devices and, more particularly, to systems and method for processing image data obtained using an image sensor of a digital imaging device. 
     This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art. 
     In recent years, digital imaging devices have become increasing popular due, at least in part, to such devices becoming more and more affordable for the average consumer. Further, in addition to a number of stand-alone digital cameras currently available on the market, it is not uncommon for digital imaging devices to be integrated as part of another electronic device, such as a desktop or notebook computer, a cellular phone, or a portable media player. 
     To acquire image data, most digital imaging devices include an image sensor that provides a number of light-detecting elements (e.g., photodetectors) configured to convert light detected by the image sensor into an electrical signal. An image sensor may also include a color filter array that filters light captured by the image sensor to capture color information. The image data captured by the image sensor may then be processed by an image processing pipeline, which may apply a number of various image processing operations to the image data to generate a full color image that may be displayed for viewing on a display device, such as a monitor. 
     While conventional image processing techniques generally aim to produce a viewable image that is both objectively and subjectively pleasing to a viewer, such conventional techniques may not adequately address errors and/or distortions in the image data introduced by the imaging device and/or the image sensor. For instance, defective pixels on the image sensor, which may be due to manufacturing defects or operational failure, may fail to sense light levels accurately and, if not corrected, may manifest as artifacts appearing in the resulting processed image. Additionally, light intensity fall-off at the edges of the image sensor, which may be due to imperfections in the manufacture of the lens, may adversely affect characterization measurements and may result in an image in which the overall light intensity is non-uniform. The image processing pipeline may also perform one or more processes to sharpen the image. Conventional sharpening techniques, however, may not adequately account for existing noise in the image signal, or may be unable to distinguish the noise from edges and textured areas in the image. In such instances, conventional sharpening techniques may actually increase the appearance of noise in the image, which is generally undesirable. Further, various additional image processing steps, some of which may rely on image statistics collected by a statistics collection engine, may also be performed. 
     Another image processing operation that may be applied to the image data captured by the image sensor is a demosaicing operation. Because the color filter array generally provides color data at one wavelength per sensor pixel, a full set of color data is generally interpolated for each color channel in order to reproduce a full color image (e.g., RGB image). Conventional demosaicing techniques generally interpolate values for the missing color data in a horizontal or a vertical direction, generally depending on some type of fixed threshold. However, such conventional demosaicing techniques may not adequately account for the locations and direction of edges within the image, which may result in edge artifacts, such as aliasing, checkerboard artifacts, or rainbow artifacts, being introduced into the full color image, particularly along diagonal edges within the image. 
     Accordingly, various considerations should be addressed when processing a digital image obtained with a digital camera or other imaging device in order to improve the appearance of the resulting image. In particular, certain aspects of the disclosure below may address one or more of the drawbacks briefly mentioned above. 
     SUMMARY 
     A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. 
     The present disclosure provides various techniques for collecting and processing statistics data in an image signal processor (ISP). In one embodiment, a statistics collection engine may be implemented in a front-end processing unit of the ISP, such that statistics are collected prior to processing by an ISP pipeline downstream from the front-end processing unit. In accordance with one aspect of the disclosure, the statistics collection engine may be configured to acquire statistics relating to auto white-balance, auto-exposure, and auto-focus. In one embodiment, the statistics collection engine may receive raw Bayer RGB data acquired by an image sensor and may be configured to perform one or more color space conversions to obtain pixel data in other color spaces. A set of pixel filters may be configured to accumulate sums of the pixel data conditionally based upon YC1C2 characteristics, as defined by a pixel condition per pixel filter. Depending on a selected color space, the pixel filters may generate color sums, which may be used to match a current illuminant against a set of reference illuminants with which the image sensor has been previously calibrated. 
     In accordance with another aspect of the disclosure, auto-focus statistics may be used to generate coarse and fine auto-focus scores for determining an optimal focal length at which to position a lens associated with the image sensor. For instance, the statistics logic may determine a coarse position that indicates an optimal focus area which, in one embodiment, may be determined by searching for the first coarse position in which a coarse auto-focus score decreases with respect to a previous position. Using this position as a starting point for fine score searching, the optimal focal position may be determined by searching for a peak in fine auto-focus scores. Auto-focus statistics may also be determined based on each color of the Bayer RGB, such that, even in the presence of chromatic aberrations, relative auto-focus scores for each color may be used to determine the direction of focus. Further, collected statistics may be output to a memory and used by the ISP to process acquired image data. 
     Various refinements of the features noted above may exist in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. Again, the brief summary presented above is intended only to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee. 
       Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which: 
         FIG. 1  is a simplified block diagram depicting components of an example of an electronic device that includes an imaging device and image processing circuitry configured to implement one or more of the image processing technique set forth in the present disclosure; 
         FIG. 2  shows a graphical representation of a 2×2 pixel block of a Bayer color filter array that may be implemented in the imaging device of  FIG. 1 ; 
         FIG. 3  is a perspective view of the electronic device of  FIG. 1  in the form of a laptop computing device, in accordance with aspects of the present disclosure; 
         FIG. 4  is a front view of the electronic device of  FIG. 1  in the form of a desktop computing device, in accordance with aspects of the present disclosure; 
         FIG. 5  is a front view of the electronic device of  FIG. 1  in the form of a handheld portable electronic device, in accordance with aspects of the present disclosure; 
         FIG. 6  is a rear view of the electronic device shown in  FIG. 5 ; 
         FIG. 7  is a block diagram illustrating front-end image signal processing (ISP) logic and ISP pipe processing logic that may be implemented in the image processing circuitry of  FIG. 1 , in accordance with aspects of the present disclosure; 
         FIG. 8  is a more detailed block diagram showing an embodiment of the ISP front-end logic of  FIG. 7 , in accordance with aspects of the present disclosure; 
         FIG. 9  is flow chart depicting a method for processing image data in the ISP front-end logic of  FIG. 8 , in accordance with an embodiment 
         FIG. 10  is block diagram illustrating a configuration of double buffered registers and control registers that may be utilized for processing image data in the ISP front-end logic, in accordance with one embodiment; 
         FIGS. 11-13  are timing diagrams depicting different modes for triggering the processing of an image frame, in accordance with embodiments of the present techniques; 
         FIG. 14  is a diagram depicting a control register in more detail, in accordance with one embodiment; 
         FIG. 15  is a flow chart depicting a method for using a front-end pixel processing unit to process image frames when the ISP front-end logic of  FIG. 8  is operating in a single sensor mode; 
         FIG. 16  is a flow chart depicting a method for using a front-end pixel processing unit to process image frames when the ISP front-end logic of  FIG. 8  is operating in a dual sensor mode; 
         FIG. 17  is a flow chart depicting a method for using a front-end pixel processing unit to process image frames when the ISP front-end logic of  FIG. 8  is operating in a dual sensor mode; 
         FIG. 18  is a flow chart depicting a method in which both image sensors are active, but wherein a first image sensor is sending image frames to a front-end pixel processing unit, while the second image sensor is sending image frames to a statistics processing unit so that imaging statistics for the second sensor are immediately available when the second image sensor continues sending image frames to the front-end pixel processing unit at a later time, in accordance with one embodiment. 
         FIG. 19  is graphical depiction of various imaging regions that may be defined within a source image frame captured by an image sensor, in accordance with aspects of the present disclosure; 
         FIG. 20  is a block diagram that provides a more detailed view of one embodiment of the ISP front-end pixel processing unit, as shown in the ISP front-end logic of  FIG. 8 , in accordance with aspects of the present disclosure; 
         FIG. 21  is a process diagram illustrating how temporal filtering may be applied to image pixel data received by the ISP front-end pixel processing unit shown in  FIG. 20 , in accordance with one embodiment; 
         FIG. 22  illustrates a set of reference image pixels and a set of corresponding current image pixels that may be used to determine one or more parameters for the temporal filtering process shown in  FIG. 21 ; 
         FIG. 23  is a flow chart illustrating a process for applying temporal filtering to a current image pixel of a set of image data, in accordance with one embodiment; 
         FIG. 24  is a flow chart showing a technique for calculating a motion delta value for use with the temporal filtering of the current image pixel of  FIG. 23 , in accordance with one embodiment; 
         FIG. 25  is a flow chart illustrating another process for applying temporal filtering to a current image pixel of a set of image data that includes the use of different gains for each color component of the image data, in accordance with another embodiment; 
         FIG. 26  is a process diagram illustrating a how a temporal filtering technique that utilizes separate motion and luma tables for each color component of the image pixel data received by the ISP front-end pixel processing unit shown in  FIG. 20 , in accordance with a further embodiment; 
         FIG. 27  is a flow chart illustrating a process for applying temporal filtering to a current image pixel of a set of image data using the motion and luma tables shown in  FIG. 26 , in accordance with further embodiment; 
         FIG. 28  depicts a sample of full resolution raw image data that may be captured by an image sensor, in accordance with aspects of the present disclosure; 
         FIG. 29  illustrates an image sensor that may be configured to apply binning to the full resolution raw image data of  FIG. 28  to output a sample of binned raw image data, in accordance with an embodiment of the present disclosure; 
         FIG. 30  depicts a sample of binned raw image data that may be provided by the image sensor of  FIG. 29 , in accordance with aspects of the present disclosure; 
         FIG. 31  depicts the binned raw image data from  FIG. 30  after being re-sampled by a binning compensation filter to provide, in accordance with aspects of the present disclosure; 
         FIG. 32  depicts a binning compensation filter that may be implemented in the ISP front-end pixel processing unit of  FIG. 20 , in accordance with one embodiment; 
         FIG. 33  is a graphical depiction of various step sizes that may be applied to a differential analyzer to select center input pixels and index/phases for binning compensation filtering, in accordance with aspects of the present disclosure; 
         FIG. 34  is a flow chart illustrating a process for scaling image data using the binning compensation filter of  FIG. 32 , in accordance with one embodiment; 
         FIG. 35  is a flow chart illustrating a process for determining a current input source center pixel for horizontal and vertical filtering by the binning compensation filter of  FIG. 32 , in accordance with one embodiment; 
         FIG. 36  is a flow chart illustrating a process for determining an index for selecting filtering coefficients for horizontal and vertical filtering by the binning compensation filter of  FIG. 32 , in accordance with one embodiment. 
         FIG. 37  is more a more detailed block diagram showing an embodiment of a statistics processing unit which may be implemented in the ISP front-end processing logic, as shown in  FIG. 8 , in accordance with aspects of the present disclosure; 
         FIG. 38  shows various image frame boundary cases that may be considered when applying techniques for detecting and correcting defective pixels during statistics processing by the statistics processing unit of  FIG. 37 , in accordance with aspects of the present disclosure; 
         FIG. 39  is a flow chart illustrating a process for performing defective pixel detection and correction during statistics processing, in accordance with one embodiment; 
         FIG. 40  shows a three-dimensional profile depicting light intensity versus pixel position for a conventional lens of an imaging device; 
         FIG. 41  is a colored drawing that exhibits non-uniform light intensity across the image, which may be the result of lens shading irregularities; 
         FIG. 42  is a graphical illustration of a raw imaging frame that includes a lens shading correction region and a gain grid, in accordance with aspects of the present disclosure; 
         FIG. 43  illustrates the interpolation of a gain value for an image pixel enclosed by four bordering grid gain points, in accordance with aspects of the present disclosure; 
         FIG. 44  is a flow chart illustrating a process for determining interpolated gain values that may be applied to imaging pixels during a lens shading correction operation, in accordance with an embodiment of the present technique; 
         FIG. 45  is a three-dimensional profile depicting interpolated gain values that may be applied to an image that exhibits the light intensity characteristics shown in  FIG. 40  when performing lens shading correction, in accordance with aspects of the present disclosure; 
         FIG. 46  shows the colored drawing from  FIG. 41  that exhibits improved uniformity in light intensity after a lens shading correction operation is applied, in accordance with accordance aspects of the present disclosure; 
         FIG. 47  graphically illustrates how a radial distance between a current pixel and the center of an image may be calculated and used to determine a radial gain component for lens shading correction, in accordance with one embodiment; 
         FIG. 48  is a flow chart illustrating a process by which radial gains and interpolated gains from a gain grid are used to determine a total gain that may be applied to imaging pixels during a lens shading correction operation, in accordance with an embodiment of the present technique; 
         FIG. 49  is a graph showing white areas and low and high color temperature axes in a color space; 
         FIG. 50  is a table showing how white balance gains may be configured for various reference illuminant conditions, in accordance with one embodiment; 
         FIG. 51  is a block diagram showing a statistics collection engine that may be implemented in the ISP front-end processing logic, in accordance with an embodiment of the present disclosure; 
         FIG. 52  illustrates the down-sampling of raw Bayer RGB data, in accordance with aspects of the present disclosure; 
         FIG. 53  depicts a two-dimensional color histogram that may be collected by the statistics collection engine of  FIG. 51 , in accordance with one embodiment; 
         FIG. 54  depicts zooming and panning within a two-dimensional color histogram; 
         FIG. 55  is a more detailed view showing logic for implementing a pixel filter of the statistics collection engine, in accordance with one embodiment; 
         FIG. 56  is a graphical depiction of how the location of a pixel within a C1-C2 color space may be evaluated based on a pixel condition defined for a pixel filter, in accordance with one embodiment; 
         FIG. 57  is a graphical depiction of how the location of a pixel within a C1-C2 color space may be evaluated based on a pixel condition defined for a pixel filter, in accordance with another embodiment; 
         FIG. 58  is a graphical depiction of how the location of a pixel within a C1-C2 color space may be evaluated based on a pixel condition defined for a pixel filter, in accordance with yet a further embodiment; 
         FIG. 59  is a graph showing how image sensor integration times may be determined to compensate for flicker, in accordance with one embodiment; 
         FIG. 60  is a detailed block diagram showing logic that may be implemented in the statistics collection engine of  FIG. 51  and configured to collect auto-focus statistics in accordance with one embodiment; 
         FIG. 61  is a graph depicting a technique for performing auto-focus using coarse and fine auto-focus scoring values, in accordance with one embodiment; 
         FIG. 62  is a flow chart depicting a process for performing auto-focus using coarse and fine auto-focus scoring values, in accordance with one embodiment; 
         FIGS. 63 and 64  show the decimation of raw Bayer data to obtain a white balanced luma value; 
         FIG. 65  shows a technique for performing auto-focus using relative auto-focus scoring values for each color component, in accordance with one embodiment; 
         FIG. 66  is a more detailed view of the statistics processing unit of  FIG. 37 , showing how Bayer RGB histogram data may be used to assist black level compensation, in accordance with one embodiment; 
         FIG. 67  is a block diagram showing an embodiment of the ISP pipe processing logic of  FIG. 7 , in accordance with aspects of the present disclosure; 
         FIG. 68  is a more detailed view showing an embodiment of a raw pixel processing block that may be implemented in the ISP pipe processing logic of  FIG. 67 , in accordance with aspects of the present disclosure; 
         FIG. 69  shows various image frame boundary cases that may be considered when applying techniques for detecting and correcting defective pixels during processing by the raw pixel processing block shown in  FIG. 68 , in accordance with aspects of the present disclosure; 
         FIGS. 70-72  are flowcharts that depict various processes for detecting and correcting defective pixels that may be performed in the raw pixel processing block of  FIG. 68 , in accordance with one embodiment; 
         FIG. 73  shows the location of two green pixels in a 2×2 pixel block of a Bayer image sensor that may be interpolated when applying green non-uniformity correction techniques during processing by the raw pixel processing logic of  FIG. 68 , in accordance with aspects of the present disclosure; 
         FIG. 74  illustrates a set of pixels that includes a center pixel and associated horizontal neighboring pixels that may be used as part of a horizontal filtering process for noise reduction, in accordance with aspects of the present disclosure; 
         FIG. 75  illustrates a set of pixels that includes a center pixel and associated vertical neighboring pixels that may be used as part of a vertical filtering process for noise reduction, in accordance with aspects of the present disclosure; 
         FIG. 76  is a simplified flow diagram that depicts how demosaicing may be applied to a raw Bayer image pattern to produce a full color RGB image; 
         FIG. 77  depicts a set of pixels of a Bayer image pattern from which horizontal and vertical energy components may be derived for interpolating green color values during demosaicing of the Bayer image pattern, in accordance with one embodiment; 
         FIG. 78  shows a set of horizontal pixels to which filtering may be applied to determine a horizontal component of an interpolated green color value during demosaicing of a Bayer image pattern, in accordance with aspects of the present technique; 
         FIG. 79  shows a set of vertical pixels to which filtering may be applied to determine a vertical component of an interpolated green color value during demosaicing of a Bayer image pattern, in accordance with aspects of the present technique; 
         FIG. 80  shows various 3×3 pixel blocks to which filtering may be applied to determine interpolated red and blue values during demosaicing of a Bayer image pattern, in accordance with aspects of the present technique; 
         FIGS. 81-84  provide flowcharts that depict various processes for interpolating green, red, and blue color values during demosaicing of a Bayer image pattern, in accordance with one embodiment; 
         FIG. 85  shows a colored drawing of an original image scene that may be captured by an image sensor and processed in accordance with aspects of the demosaicing techniques disclosed herein; 
         FIG. 86  shows a colored drawing of Bayer image pattern of the image scene shown in  FIG. 85 ; 
         FIG. 87  shows a colored drawing of an RGB image reconstructed using a conventional demosaicing technique based upon the Bayer image pattern of  FIG. 86 ; 
         FIG. 88  shows a colored drawing of an RGB image reconstructed from the Bayer image pattern of  FIG. 86  in accordance with aspects of the demosaicing techniques disclosed herein; 
         FIG. 89  is a more detailed view showing one embodiment of an RGB processing block that may be implemented in the ISP pipe processing logic of  FIG. 67 , in accordance with aspects of the present disclosure; 
         FIG. 90  is a more detailed view showing one embodiment of a YCbCr processing block that may be implemented in the ISP pipe processing logic of  FIG. 67 , in accordance with aspects of the present disclosure; 
         FIG. 91  is a graphical depiction of active source regions for luma and chroma, as defined within a source buffer using a 1-plane format, in accordance with aspects of the present disclosure; 
         FIG. 92  is a graphical depiction of active source regions for luma and chroma, as defined within a source buffer using a 2-plane format, in accordance with aspects of the present disclosure; 
         FIG. 93  is a block diagram illustrating image sharpening logic that may be implemented in the YCbCr processing block, as shown in  FIG. 90 , in accordance with one embodiment; 
         FIG. 94  is a block diagram illustrating edge enhancement logic that may be implemented in the YCbCr processing block, as shown in  FIG. 90 , in accordance with one embodiment; 
         FIG. 95  is a graph showing the relationship of chroma attenuation factors to sharpened luma values, in accordance with aspects of the present disclosure; 
         FIG. 96  is a block diagram illustrating image brightness, contrast, and color (BCC) adjustment logic that may be implemented in the YCbCr processing block, as shown in  FIG. 90 , in accordance with one embodiment; and 
         FIG. 97  shows a hue and saturation color wheel in the YCbCr color space defining various hue angles and saturation values that may be applied during color adjustment in the BCC adjustment logic shown in  FIG. 96 . 
     
    
    
     DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS 
     One or more specific embodiments of the present disclosure will be described below. These described embodiments are only examples of the presently disclosed techniques. Additionally, in an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. 
     As will be discussed below, the present disclosure relates generally to techniques for processing image data acquired via one or more image sensing devices. In particular, certain aspects of the present disclosure may relate to techniques for detecting and correcting defective pixels, techniques for demosaicing a raw image pattern, techniques for sharpening a luminance image using a multi-scale unsharp mask, and techniques for applying lens shading gains to correct for lens shading irregularities. Further, it should be understood that the presently disclosed techniques may be applied to both still images and moving images (e.g., video), and may be utilized in any suitable type of imaging application, such as a digital camera, an electronic device having an integrated digital camera, a security or video surveillance system, a medical imaging system, and so forth. 
     Keeping the above points in mind,  FIG. 1  is a block diagram illustrating an example of an electronic device  10  that may provide for the processing of image data using one or more of the image processing techniques briefly mentioned above. The electronic device  10  may be any type of electronic device, such as a laptop or desktop computer, a mobile phone, a digital media player, or the like, that is configured to receive and process image data, such as data acquired using one or more image sensing components. By way of example only, the electronic device  10  may be a portable electronic device, such as a model of an iPod® or iPhone®, available from Apple Inc. of Cupertino, Calif. Additionally, the electronic device  10  may be a desktop or laptop computer, such as a model of a MacBook®, MacBook® Pro, MacBook Air®, iMac®, Mac® Mini, or Mac Pro®, available from Apple Inc. In other embodiments, electronic device  10  may also be a model of an electronic device from another manufacturer that is capable of acquiring and processing image data. 
     Regardless of its form (e.g., portable or non-portable), it should be understood that the electronic device  10  may provide for the processing of image data using one or more of the image processing techniques briefly discussed above, which may include defective pixel correction and/or detection techniques, lens shading correction techniques, demosaicing techniques, or image sharpening techniques, among others. In some embodiments, the electronic device  10  may apply such image processing techniques to image data stored in a memory of the electronic device  10 . In further embodiments, the electronic device  10  may include one or more imaging devices, such as an integrated or external digital camera, configured to acquire image data, which may then be processed by the electronic device  10  using one or more of the above-mentioned image processing techniques. Embodiments showing both portable and non-portable embodiments of electronic device  10  will be further discussed below in  FIGS. 3-6 . 
     As shown in  FIG. 1 , the electronic device  10  may include various internal and/or external components which contribute to the function of the device  10 . Those of ordinary skill in the art will appreciate that the various functional blocks shown in  FIG. 1  may comprise hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium) or a combination of both hardware and software elements. For example, in the presently illustrated embodiment, the electronic device  10  may include input/output (I/O) ports  12 , input structures  14 , one or more processors  16 , memory device  18 , non-volatile storage  20 , expansion card(s)  22 , networking device  24 , power source  26 , and display  28 . Additionally, the electronic device  10  may include one or more imaging devices  30 , such as a digital camera, and image processing circuitry  32 . As will be discussed further below, the image processing circuitry  32  may be configured implement one or more of the above-discussed image processing techniques when processing image data. As can be appreciated, image data processed by image processing circuitry  32  may be retrieved from the memory  18  and/or the non-volatile storage device(s)  20 , or may be acquired using the imaging device  30 . 
     Before continuing, it should be understood that the system block diagram of the device  10  shown in  FIG. 1  is intended to be a high-level control diagram depicting various components that may be included in such a device  10 . That is, the connection lines between each individual component shown in  FIG. 1  may not necessarily represent paths or directions through which data flows or is transmitted between various components of the device  10 . Indeed, as discussed below, the depicted processor(s)  16  may, in some embodiments, include multiple processors, such as a main processor (e.g., CPU), and dedicated image and/or video processors. In such embodiments, the processing of image data may be primarily handled by these dedicated processors, thus effectively offloading such tasks from a main processor (CPU). 
     With regard to each of the illustrated components in  FIG. 1 , the I/O ports  12  may include ports configured to connect to a variety of external devices, such as a power source, an audio output device (e.g., headset or headphones), or other electronic devices (such as handheld devices and/or computers, printers, projectors, external displays, modems, docking stations, and so forth). In one embodiment, the I/O ports  12  may be configured to connect to an external imaging device, such as a digital camera, for the acquisition of image data that may be processed using the image processing circuitry  32 . The I/O ports  12  may support any suitable interface type, such as a universal serial bus (USB) port, a serial connection port, an IEEE-1394 (FireWire) port, an Ethernet or modem port, and/or an AC/DC power connection port. 
     In some embodiments, certain I/O ports  12  may be configured to provide for more than one function. For instance, in one embodiment, the I/O ports  12  may include a proprietary port from Apple Inc. that may function not only to facilitate the transfer of data between the electronic device  10  and an external source, but also to couple the device  10  to a power charging interface such as an power adapter designed to provide power from a electrical wall outlet, or an interface cable configured to draw power from another electrical device, such as a desktop or laptop computer, for charging the power source  26  (which may include one or more rechargeable batteries). Thus, the I/O port  12  may be configured to function dually as both a data transfer port and an AC/DC power connection port depending, for example, on the external component being coupled to the device  10  via the I/O port  12 . 
     The input structures  14  may provide user input or feedback to the processor(s)  16 . For instance, input structures  14  may be configured to control one or more functions of electronic device  10 , such as applications running on electronic device  10 . By way of example only, input structures  14  may include buttons, sliders, switches, control pads, keys, knobs, scroll wheels, keyboards, mice, touchpads, and so forth, or some combination thereof. In one embodiment, input structures  14  may allow a user to navigate a graphical user interface (GUI) displayed on device  10 . Additionally, input structures  14  may include a touch sensitive mechanism provided in conjunction with display  28 . In such embodiments, a user may select or interact with displayed interface elements via the touch sensitive mechanism. 
     The input structures  14  may include the various devices, circuitry, and pathways by which user input or feedback is provided to one or more processors  16 . Such input structures  14  may be configured to control a function of the device  10 , applications running on the device  10 , and/or any interfaces or devices connected to or used by the electronic device  10 . For example, the input structures  14  may allow a user to navigate a displayed user interface or application interface. Examples of the input structures  14  may include buttons, sliders, switches, control pads, keys, knobs, scroll wheels, keyboards, mice, touchpads, and so forth. 
     In certain embodiments, an input structure  14  and the display device  28  may be provided together, such as in the case of a “touchscreen,” whereby a touch-sensitive mechanism is provided in conjunction with the display  28 . In such embodiments, the user may select or interact with displayed interface elements via the touch-sensitive mechanism. In this way, the displayed interface may provide interactive functionality, allowing a user to navigate the displayed interface by touching the display  28 . For example, user interaction with the input structures  14 , such as to interact with a user or application interface displayed on the display  26 , may generate electrical signals indicative of the user input. These input signals may be routed via suitable pathways, such as an input hub or data bus, to the one or more processors  16  for further processing. 
     In addition to processing various input signals received via the input structure(s)  14 , the processor(s)  16  may control the general operation of the device  10 . For instance, the processor(s)  16  may provide the processing capability to execute an operating system, programs, user and application interfaces, and any other functions of the electronic device  10 . The processor(s)  16  may include one or more microprocessors, such as one or more “general-purpose” microprocessors, one or more special-purpose microprocessors and/or application-specific microprocessors (ASICs), or a combination of such processing components. For example, the processor(s)  16  may include one or more instruction set (e.g., RISC) processors, as well as graphics processors (GPU), video processors, audio processors and/or related chip sets. As will be appreciated, the processor(s)  16  may be coupled to one or more data buses for transferring data and instructions between various components of the device  10 . In certain embodiments, the processor(s)  16  may provide the processing capability to execute an imaging applications on the electronic device  10 , such as Photo Booth®, Aperture®, iPhoto®, or Preview®, available from Apple Inc., or the “Camera” and/or “Photo” applications provided by Apple Inc. and available on models of the iPhone®. 
     The instructions or data to be processed by the processor(s)  16  may be stored in a computer-readable medium, such as a memory device  18 . The memory device  18  may be provided as a volatile memory, such as random access memory (RAM) or as a non-volatile memory, such as read-only memory (ROM), or as a combination of one or more RAM and ROM devices. The memory  18  may store a variety of information and may be used for various purposes. For example, the memory  18  may store firmware for the electronic device  10 , such as a basic input/output system (BIOS), an operating system, various programs, applications, or any other routines that may be executed on the electronic device  10 , including user interface functions, processor functions, and so forth. In addition, the memory  18  may be used for buffering or caching during operation of the electronic device  10 . For instance, in one embodiment, the memory  18  include one or more frame buffers for buffering video data as it is being output to the display  28 . 
     In addition to the memory device  18 , the electronic device  10  may further include a non-volatile storage  20  for persistent storage of data and/or instructions. The non-volatile storage  20  may include flash memory, a hard drive, or any other optical, magnetic, and/or solid-state storage media, or some combination thereof. Thus, although depicted as a single device in  FIG. 1  for purposes of clarity, it should understood that the non-volatile storage device(s)  20  may include a combination of one or more of the above-listed storage devices operating in conjunction with the processor(s)  16 . The non-volatile storage  20  may be used to store firmware, data files, image data, software programs and applications, wireless connection information, personal information, user preferences, and any other suitable data. In accordance with aspects of the present disclosure, image data stored in the non-volatile storage  20  and/or the memory device  18  may be processed by the image processing circuitry  32  prior to being output on a display. 
     The embodiment illustrated in  FIG. 1  may also include one or more card or expansion slots. The card slots may be configured to receive an expansion card  22  that may be used to add functionality, such as additional memory, I/O functionality, or networking capability, to the electronic device  10 . Such an expansion card  22  may connect to the device through any type of suitable connector, and may be accessed internally or external with respect to a housing of the electronic device  10 . For example, in one embodiment, the expansion card  24  may be flash memory card, such as a SecureDigital (SD) card, mini- or microSD, CompactFlash card, or the like, or may be a PCMCIA device. Additionally, the expansion card  24  may be a Subscriber Identity Module (SIM) card, for use with an embodiment of the electronic device  10  that provides mobile phone capability. 
     The electronic device  10  also includes the network device  24 , which may be a network controller or a network interface card (NIC) that may provide for network connectivity over a wireless 802.11 standard or any other suitable networking standard, such as a local area network (LAN), a wide area network (WAN), such as an Enhanced Data Rates for GSM Evolution (EDGE) network, a 3G data network, or the Internet. In certain embodiments, the network device  24  may provide for a connection to an online digital media content provider, such as the iTunes® music service, available from Apple Inc. 
     The power source  26  of the device  10  may include the capability to power the device  10  in both non-portable and portable settings. For example, in a portable setting, the device  10  may include one or more batteries, such as a Li-Ion battery, for powering the device  10 . The battery may be re-charged by connecting the device  10  to an external power source, such as to an electrical wall outlet. In a non-portable setting, the power source  26  may include a power supply unit (PSU) configured to draw power from an electrical wall outlet, and to distribute the power to various components of a non-portable electronic device, such as a desktop computing system. 
     The display  28  may be used to display various images generated by device  10 , such as a GUI for an operating system, or image data (including still images and video data) processed by the image processing circuitry  32 , as will be discussed further below. As mentioned above, the image data may include image data acquired using the imaging device  30  or image data retrieved from the memory  18  and/or non-volatile storage  20 . The display  28  may be any suitable type of display, such as a liquid crystal display (LCD), plasma display, or an organic light emitting diode (OLED) display, for example. Additionally, as discussed above, the display  28  may be provided in conjunction with the above-discussed touch-sensitive mechanism (e.g., a touch screen) that may function as part of a control interface for the electronic device  10 . 
     The illustrated imaging device(s)  30  may be provided as a digital camera configured to acquire both still images and moving images (e.g., video). The camera  30  may include a lens and one or more image sensors configured to capturing and converting light into electrical signals. By way of example only, the image sensor may include a CMOS image sensor (e.g., a CMOS active-pixel sensor (APS)) or a CCD (charge-coupled device) sensor. Generally, the image sensor in the camera  30  includes an integrated circuit having an array of pixels, wherein each pixel includes a photodetector for sensing light. As those skilled in the art will appreciate, the photodetectors in the imaging pixels generally detect the intensity of light captured via the camera lenses. However, photodetectors, by themselves, are generally unable to detect the wavelength of the captured light and, thus, are unable to determine color information. 
     Accordingly, the image sensor may further include a color filter array (CFA) that may overlay or be disposed over the pixel array of the image sensor to capture color information. The color filter array may include an array of small color filters, each of which may overlap a respective pixel of the image sensor and filter the captured light by wavelength. Thus, when used in conjunction, the color filter array and the photodetectors may provide both wavelength and intensity information with regard to light captured through the camera, which may be representative of a captured image. 
     In one embodiment, the color filter array may include a Bayer color filter array, which provides a filter pattern that is 50% green elements, 25% red elements, and 25% blue elements. For instance,  FIG. 2  shows a 2×2 pixel block of a Bayer CFA includes 2 green elements (Gr and Gb), 1 red element (R), and 1 blue element (B). Thus, an image sensor that utilizes a Bayer color filter array may provide information regarding the intensity of the light received by the camera  30  at the green, red, and blue wavelengths, whereby each image pixel records only one of the three colors (RGB). This information, which may be referred to as “raw image data” or data in the “raw domain,” may then be processed using one or more demosaicing techniques to convert the raw image data into a full color image, generally by interpolating a set of red, green, and blue values for each pixel. As will be discussed further below, such demosaicing techniques may be performed by the image processing circuitry  32 . 
     As mentioned above, the image processing circuitry  32  may provide for various image processing steps, such as defective pixel detection/correction, lens shading correction, demosaicing, and image sharpening, noise reduction, gamma correction, image enhancement, color-space conversion, image compression, chroma sub-sampling, and image scaling operations, and so forth. In some embodiments, the image processing circuitry  32  may include various subcomponents and/or discrete units of logic that collectively form an image processing “pipeline” for performing each of the various image processing steps. These subcomponents may be implemented using hardware (e.g., digital signal processors or ASICs) or software, or via a combination of hardware and software components. The various image processing operations that may be provided by the image processing circuitry  32  and, particularly those processing operations relating to defective pixel detection/correction, lens shading correction, demosaicing, and image sharpening, will be discussed in greater detail below. 
     Before continuing, it should be noted that while various embodiments of the various image processing techniques discussed below may utilize a Bayer CFA, the presently disclosed techniques are not intended to be limited in this regard. Indeed, those skilled in the art will appreciate that the image processing techniques provided herein may be applicable to any suitable type of color filter array, including RGBW filters, CYGM filters, and so forth. 
     Referring again to the electronic device  10 ,  FIGS. 3-6  illustrate various forms that the electronic device  10  may take. As mentioned above, the electronic device  10  may take the form of a computer, including computers that are generally portable (such as laptop, notebook, and tablet computers) as well as computers that are generally non-portable (such as desktop computers, workstations and/or servers), or other type of electronic device, such as handheld portable electronic devices (e.g., digital media player or mobile phone). In particular,  FIGS. 3 and 4  depict the electronic device  10  in the form of a laptop computer  40  and a desktop computer  50 , respectively.  FIGS. 5 and 6  show front and rear views, respectively, of the electronic device  10  in the form of a handheld portable device  60 . 
     As shown in  FIG. 3 , the depicted laptop computer  40  includes a housing  42 , the display  28 , the I/O ports  12 , and the input structures  14 . The input structures  14  may include a keyboard and a touchpad mouse that are integrated with the housing  42 . Additionally, the input structure  14  may include various other buttons and/or switches which may be used to interact with the computer  40 , such as to power on or start the computer, to operate a GUI or an application running on the computer  40 , as well as adjust various other aspects relating to operation of the computer  40  (e.g., sound volume, display brightness, etc.). The computer  40  may also include various I/O ports  12  that provide for connectivity to additional devices, as discussed above, such as a FireWire® or USB port, a high definition multimedia interface (HDMI) port, or any other type of port that is suitable for connecting to an external device. Additionally, the computer  40  may include network connectivity (e.g., network device  26 ), memory (e.g., memory  20 ), and storage capabilities (e.g., storage device  22 ), as described above with respect to  FIG. 1 . 
     Further, the laptop computer  40 , in the illustrated embodiment, may include an integrated imaging device  30  (e.g., camera). In other embodiments, the laptop computer  40  may utilize an external camera (e.g., an external USB camera or a “webcam”) connected to one or more of the I/O ports  12  instead of or in addition to the integrated camera  30 . For instance, an external camera may be an iSight® camera available from Apple Inc. The camera  30 , whether integrated or external, may provide for the capture and recording of images. Such images may then be viewed by a user using an image viewing application, or may be utilized by other applications, including video-conferencing applications, such as iChat®, and image editing/viewing applications, such as Photo Booth®, Aperture®, iPhoto®, or Preview®, which are available from Apple Inc. In certain embodiments, the depicted laptop computer  40  may be a model of a MacBook®, MacBook® Pro, MacBook Air®, or PowerBook® available from Apple Inc. Additionally, the computer  40 , in one embodiment, may be a portable tablet computing device, such as a model of an iPad® tablet computer, also available from Apple Inc. 
       FIG. 4  further illustrates an embodiment in which the electronic device  10  is provided as a desktop computer  50 . As will be appreciated, the desktop computer  50  may include a number of features that may be generally similar to those provided by the laptop computer  40  shown in  FIG. 4 , but may have a generally larger overall form factor. As shown, the desktop computer  50  may be housed in an enclosure  42  that includes the display  28 , as well as various other components discussed above with regard to the block diagram shown in  FIG. 1 . Further, the desktop computer  50  may include an external keyboard and mouse (input structures  14 ) that may be coupled to the computer  50  via one or more I/O ports  12  (e.g., USB) or may communicate with the computer  50  wirelessly (e.g., RF, Bluetooth, etc.). The desktop computer  50  also includes an imaging device  30 , which may be an integrated or external camera, as discussed above. In certain embodiments, the depicted desktop computer  50  may be a model of an iMac®, Mac® mini, or Mac Pro®, available from Apple Inc. 
     As further shown, the display  28  may be configured to generate various images that may be viewed by a user. For example, during operation of the computer  50 , the display  28  may display a graphical user interface (“GUI”)  52  that allows the user to interact with an operating system and/or application running on the computer  50 . The GUI  52  may include various layers, windows, screens, templates, or other graphical elements that may be displayed in all, or a portion, of the display device  28 . For instance, in the depicted embodiment, an operating system GUI  52  may include various graphical icons  54 , each of which may correspond to various applications that may be opened or executed upon detecting a user selection (e.g., via keyboard/mouse or touchscreen input). The icons  54  may be displayed in a dock  56  or within one or more graphical window elements  58  displayed on the screen. In some embodiments, the selection of an icon  54  may lead to a hierarchical navigation process, such that selection of an icon  54  leads to a screen or opens another graphical window that includes one or more additional icons or other GUI elements. By way of example only, the operating system GUI  52  displayed in  FIG. 4  may be from a version of the Mac OS® operating system, available from Apple Inc. 
     Continuing to  FIGS. 5 and 6 , the electronic device  10  is further illustrated in the form of portable handheld electronic device  60 , which may be a model of an iPod® or iPhone® available from Apple Inc. In the depicted embodiment, the handheld device  60  includes an enclosure  42 , which may function to protect the interior components from physical damage and to shield them from electromagnetic interference. The enclosure  42  may be formed from any suitable material or combination of materials, such as plastic, metal, or a composite material, and may allow certain frequencies of electromagnetic radiation, such as wireless networking signals, to pass through to wireless communication circuitry (e.g., network device  24 ), which may be disposed within the enclosure  42 , as shown in  FIG. 5 . 
     The enclosure  42  also includes various user input structures  14  through which a user may interface with the handheld device  60 . For instance, each input structure  14  may be configured to control one or more respective device functions when pressed or actuated. By way of example, one or more of the input structures  14  may be configured to invoke a “home” screen  42  or menu to be displayed, to toggle between a sleep, wake, or powered on/off mode, to silence a ringer for a cellular phone application, to increase or decrease a volume output, and so forth. It should be understood that the illustrated input structures  14  are merely exemplary, and that the handheld device  60  may include any number of suitable user input structures existing in various forms including buttons, switches, keys, knobs, scroll wheels, and so forth. 
     As shown in  FIG. 5 , the handheld device  60  may include various I/O ports  12 . For instance, the depicted I/O ports  12  may include a proprietary connection port  12   a  for transmitting and receiving data files or for charging a power source  26  and an audio connection port  12   b  for connecting the device  60  to an audio output device (e.g., headphones or speakers). Further, in embodiments where the handheld device  60  provides mobile phone functionality, the device  60  may include an I/O port  12   c  for receiving a subscriber identify module (SIM) card (e.g., an expansion card  22 ). 
     The display device  28 , which may be an LCD, OLED, or any suitable type of display, may display various images generated by the handheld device  60 . For example, the display  28  may display various system indicators  64  providing feedback to a user with regard to one or more states of handheld device  60 , such as power status, signal strength, external device connections, and so forth. The display may also display a GUI  52  that allows a user to interact with the device  60 , as discussed above with reference to  FIG. 4 . The GUI  52  may include graphical elements, such as the icons  54  which may correspond to various applications that may be opened or executed upon detecting a user selection of a respective icon  54 . By way of example, one of the icons  54  may represent a camera application  66  that may be used in conjunction with a camera  30  (shown in phantom lines in  FIG. 5 ) for acquiring images. Referring briefly to  FIG. 6 , a rear view of the handheld electronic device  60  depicted in  FIG. 5  is illustrated, which shows the camera  30  as being integrated with the housing  42  and positioned on the rear of the handheld device  60 . 
     As mentioned above, image data acquired using the camera  30  may be processed using the image processing circuitry  32 , which my include hardware (e.g., disposed within the enclosure  42 ) and/or software stored on one or more storage devices (e.g., memory  18  or non-volatile storage  20 ) of the device  60 . Images acquired using the camera application  66  and the camera  30  may be stored on the device  60  (e.g., in storage device  20 ) and may be viewed at a later time using a photo viewing application  68 . 
     The handheld device  60  may also include various audio input and output elements. For example, the audio input/output elements, depicted generally by reference numeral  70 , may include an input receiver, such as one or more microphones. For instance, where the handheld device  60  includes cell phone functionality, the input receivers may be configured to receive user audio input, such as a user&#39;s voice. Additionally, the audio input/output elements  70  may include one or more output transmitters. Such output transmitters may include one or more speakers which may function to transmit audio signals to a user, such as during the playback of music data using a media player application  72 . Further, in embodiments where the handheld device  60  includes a cell phone application, an additional audio output transmitter  74  may be provided, as shown in  FIG. 5 . Like the output transmitters of the audio input/output elements  70 , the output transmitter  74  may also include one or more speakers configured to transmit audio signals to a user, such as voice data received during a telephone call. Thus, the audio input/output elements  70  and  74  may operate in conjunction to function as the audio receiving and transmitting elements of a telephone. 
     Having now provided some context with regard to various forms that the electronic device  10  may take, the present discussion will now focus on the image processing circuitry  32  depicted in  FIG. 1 . As mentioned above, the image processing circuitry  32  may be implemented using hardware and/or software components, and may include various processing units that define an image signal processing (ISP) pipeline. In particular, the following discussion may focus on aspects of the image processing techniques set forth in the present disclosure, particularly those relating to defective pixel detection/correction techniques, lens shading correction techniques, demosaicing techniques, and image sharpening techniques. 
     Referring now to  FIG. 7 , a simplified top-level block diagram depicting several functional components that may be implemented as part of the image processing circuitry  32  is illustrated, in accordance with one embodiment of the presently disclosed techniques. Particularly,  FIG. 7  is intended to illustrate how image data may flow through the image processing circuitry  32 , in accordance with at least one embodiment. In order to provide a general overview of the image processing circuitry  32 , a general description of how these functional components operate to process image data is provided here with reference to  FIG. 7 , while a more specific description of each of the illustrated functional components, as well as their respective sub-components, will be further provided below. 
     Referring to the illustrated embodiment, the image processing circuitry  32  may include image signal processing (ISP) front-end processing logic  80 , ISP pipe processing logic  82 , and control logic  84 . Image data captured by the imaging device  30  may first be processed by the ISP front-end logic  80  and analyzed to capture image statistics that may be used to determine one or more control parameters for the ISP pipe logic  82  and/or the imaging device  30 . The ISP front-end logic  80  may be configured to capture image data from an image sensor input signal. For instance, as shown in  FIG. 7 , the imaging device  30  may include a camera having one or more lenses  88  and image sensor(s)  90 . As discussed above, the image sensor(s)  90  may include a color filter array (e.g., a Bayer filter) and may thus provide both light intensity and wavelength information captured by each imaging pixel of the image sensors  90  to provide for a set of raw image data that may be processed by the ISP front-end logic  80 . For instance, the output  92  from the imaging device  30  may be received by a sensor interface  94 , which may then provide the raw image data  96  to the ISP front-end logic  80  based, for example, on the sensor interface type. By way of example, the sensor interface  94  may utilize a Standard Mobile Imaging Architecture (SMIA) interface or other serial or parallel camera interfaces, or some combination thereof. In certain embodiments, the ISP front-end logic  80  may operate within its own clock domain and may provide an asynchronous interface to the sensor interface  94  to support image sensors of different sizes and timing requirements. 
     The raw image data  96  may be provided to the ISP front-end logic  80  and processed on a pixel-by-pixel basis in a number of formats. For instance, each image pixel may have a bit-depth of 8, 10, 12, or 14 bits. The ISP front-end logic  80  may perform one or more image processing operations on the raw image data  96 , as well as collect statistics about the image data  96 . The image processing operations, as well as the collection of statistical data, may be performed at the same or at different bit-depth precisions. For example, in one embodiment, processing of the raw image pixel data  96  may be performed at a precision of 14-bits. In such embodiments, raw pixel data received by the ISP front-end logic  80  that has a bit-depth of less than 14 bits (e.g., 8-bit, 10-bit, 12-bit) may be up-sampled to 14-bits for image processing purposes. In another embodiment, statistical processing may occur at a precision of 8-bits and, thus, raw pixel data having a higher bit-depth may be down-sampled to an 8-bit format for statistics purposes. As will be appreciated, down-sampling to 8-bits may reduce hardware size (e.g., area) and also reduce processing/computational complexity for the statistics data. Additionally, the raw image data may be averaged spatially to allow for the statistics data to be more robust to noise. 
     Further, as shown in  FIG. 7 , the ISP front-end logic  80  may also receive pixel data from the memory  108 . For instance, as shown by reference number  98 , the raw pixel data may be sent to the memory  108  from the sensor interface  94 . The raw pixel data residing in the memory  108  may then be provided to the ISP front-end logic  80  for processing, as indicated by reference number  100 . The memory  108  may be part of the memory device  18 , the storage device  20 , or may be a separate dedicated memory within the electronic device  10  and may include direct memory access (DMA) features. Further, in certain embodiments, the ISP front-end logic  80  may operate within its own clock domain and provide an asynchronous interface to the sensor interface  94  to support sensors of different sizes and having different timing requirements. 
     Upon receiving the raw image data  96  (from sensor interface  94 ) or  100  (from memory  108 ), the ISP front-end logic  80  may perform one or more image processing operations, such as temporal filtering and/or binning compensation filtering. The processed image data may then be provided to the ISP pipe logic  82  (output signal  109 ) for additional processing prior to being displayed (e.g., on display device  28 ), or may be sent to the memory (output signal  110 ). The ISP pipe logic  82  receives the “front-end” processed data, either directly form the ISP front-end logic  80  or from the memory  108  (input signal  112 ), and may provide for additional processing of the image data in the raw domain, as well as in the RGB and YCbCr color spaces. Image data processed by the ISP pipe logic  82  may then be output (signal  114 ) to the display  28  for viewing by a user and/or may be further processed by a graphics engine or GPU. Additionally, output from the ISP pipe logic  82  may be sent to memory  108  (signal  115 ) and the display  28  may read the image data from memory  108  (signal  116 ), which may, in certain embodiments, be configured to implement one or more frame buffers. Further, in some implementations, the output of the ISP pipe logic  82  may also be provided to a compression/decompression engine  118  (signal  117 ) for encoding/decoding the image data. The encoded image data may be stored and then later decompressed prior to being displayed on the display  28  device (signal  119 ). By way of example, the compression engine or “encoder”  118  may be a JPEG compression engine for encoding still images, or an H.264 compression engine for encoding video images, or some combination thereof, as well as a corresponding decompression engine for decoding the image data. Additional information with regard to image processing operations that may be provided in the ISP pipe logic  82  will be discussed in greater detail below with regard to  FIGS. 67-97 . Also, it should be noted that the ISP pipe logic  82  may also receive raw image data from the memory  108 , as depicted by input signal  112 . 
     Statistical data  102  determined by the ISP front-end logic  80  may be provided to a control logic unit  84 . The statistical data  102  may include, for example, image sensor statistics relating to auto-exposure, auto-white balance, auto-focus, flicker detection, black level compensation (BLC), lens shading correction, and so forth. The control logic  84  may include a processor and/or microcontroller configured to execute one or more routines (e.g., firmware) that may be configured to determine, based upon the received statistical data  102 , control parameters  104  for the imaging device  30 , as well as control parameters  106  for the ISP pipe processing logic  82 . By way of example only, the control parameters  104  may include sensor control parameters (e.g., gains, integration time for exposure control), camera flash control parameters, lens control parameters (e.g., focal length for focusing or zoom), or a combination of such parameters. The ISP control parameters  106  may include gain levels and color correction matrix (CCM) coefficients for auto-white balance and color adjustment (e.g., during RGB processing), as well as lens shading correction parameters which, as discussed below, may be determined based upon white point balance parameters. In some embodiments, the control logic  84  may, in addition to analyzing statistics data  102 , also analyze historical statistics, which may be stored on the electronic device  10  (e.g., in memory  18  or storage  20 ). 
     Due to the generally complex design of the image processing circuitry  32  shown herein, it may be beneficial to separate the discussion of the ISP front-end logic  80  and the ISP pipe processing logic  82  into separate sections, as shown below. Particularly,  FIGS. 8 to 66  of the present application may relate to the discussion of various embodiments and aspects of the ISP front-end logic  80 , while  FIGS. 67 to 97  of the present application may relate to the discussion of various embodiments and aspects of the ISP pipe processing logic  82 . 
     The ISP Front-End Processing Logic 
       FIG. 8  is a more detailed block diagram showing functional logic blocks that may be implemented in the ISP front-end logic  80 , in accordance with one embodiment. Depending on the configuration of the imaging device  30  and/or sensor interface  94 , as discussed above in  FIG. 7 , raw image data may be provided to the ISP front-end logic  80  by one or more image sensors  90 . In the depicted embodiment, raw image data may be provided to the ISP front-end logic  80  by a first image sensor  90   a  (Sensor 0 ) and a second image sensor  90   b  (Sensor 1 ). As will be discussed further below, each image sensor  90   a  and  90   b  may be configured to apply binning to full resolution image data in order to increase signal-to-noise ratio of the image signal. For instance, a binning technique, such as 2×2 binning, may be applied which may interpolate a “binned” raw image pixel based upon four full-resolution image pixels of the same color. In one embodiment, this may result in there being four accumulated signal components associated with the binned pixel versus a single noise component, thus improving signal-to-noise of the image data, but reducing overall resolution. Additionally, binning may also result in an uneven or non-uniform spatial sampling of the image data, which may be corrected using binning compensation filtering, as will be discussed in more detail below. 
     As shown, the image sensors  90   a  and  90   b  may provide the raw image data as signals Sif 0  and Sif 1 , respectively. Each of the image sensors  90   a  and  90   b  may be generally associated with the respective statistics processing units  120  (StatsPipe 0 ) and  122  (StatsPipe 1 ), which may be configured to process image data for the determination of one or more sets of statistics (as indicated by signals Stats 0  and Stats 1 ), including statistics relating to auto-exposure, auto-white balance, auto-focus, flicker detection, black level compensation, and lens shading correction, and so forth. In certain embodiments, when only one of the sensors  90   a  or  90   b  is actively acquiring image, the image data may be sent to both StatsPipe 0  and StatsPipe 1  if additional statistics are desired. For instance, to provide one example, if StatsPipe 0  and StatsPipe 1  are both available, StatsPipe 0  may be utilized to collect statistics for one color space (e.g., RGB), and StatsPipe 1  may be utilized to collect statistics for another color space (e.g., YUV or YCbCr). That is, the statistics process units  120  and  122  may operate in parallel to collect multiple sets of statistics for each frame of the image data acquired by the active sensor. 
     In the present embodiment, five asynchronous sources of data are provided in the ISP front-end  80 . These include: (1) a direct input from a sensor interface corresponding to Sensor 0  ( 90   a ) (referred to as Sif 0  or Sens 0 ), (2) a direct input from a sensor interface corresponding to Sensor 1  ( 90   b ) (referred to as Sif 1  or Sens 1 ), (3) Sensor 0  data input from the memory  108  (referred to as SifIn 0  or Sens 0 DMA), which may include a DMA interface, (4) Sensor 1  data input from the memory  108  (referred to as SifIn 1  or Sens 1 DMA), and (5) a set of image data with frames from Sensor 0  and Sensor 1  data input retrieved from the memory  108  (referred to as FeProcIn or ProcInDMA). The ISP front-end  80  may also include multiple destinations to which image data from the sources may be routed, wherein each destination may be either a storage location in memory (e.g., in  108 ), or a processing unit. For instance, in the present embodiment, the ISP front-end  80  includes six destinations: (1) Sif 0 DMA for receiving Sensor 0  data in the memory  108 , (2) Sif 1 DMA for receiving Sensor 1  data in the memory  108 , (3) the first statistics processing unit  120  (StatsPipe 0 ), (4) the second statistics processing unit  122  (StatsPipe 1 ), (5) the front-end pixel processing unit (FEProc)  130 , and (6) FeOut (or FEProcOut) to memory  108  or the ISP pipeline  82  (discussed in further detail below). In one embodiment, the ISP front-end  80  may be configured such that only certain destinations are valid for a particular source, as shown in Table 1 below. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Example of ISP Front-end valid destinations for each source 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 SIf0- 
                 SIf1- 
                   
                   
                   
                   
               
               
                   
                 DMA 
                 DMA 
                 StatsPipe0 
                 StatsPipe1 
                 FEProc 
                 FEOut 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Sens0 
                 X 
                   
                 X 
                 X 
                 X 
                 X 
               
               
                 Sens1 
                   
                 X 
                 X 
                 X 
                 X 
                 X 
               
               
                 Sens0DMA 
                   
                   
                 X 
               
               
                 Sens1DMA 
                   
                   
                   
                 X 
               
               
                 ProcInDMA 
                   
                   
                   
                   
                 X 
                 X 
               
               
                   
               
            
           
         
       
     
     For instance, in accordance with Table 1, source Sens 0  (sensor interface of Sensor 0 ) may be configured to provide data to destinations SIf 0 DMA (signal  134 ), StatsPipe 0  (signal  136 ), StatsPipe 1  (signal  138 ), FEProc (signal  140 ), or FEOut (signal  142 ). With regard to FEOut, source data may, in some instances, be provided to FEOut to bypass pixel processing by FEProc, such as for debugging or test purposes. Additionally, source Sens 1  (sensor interface of Sensor 1 ) may be configured to provide data to destinations SIf 1 DMA (signal  144 ), StatsPipe 0  (signal  146 ), StatsPipe 1  (signal  148 ), FEProc (signal  150 ), or FEOut (signal  152 ), source Sens 0 DMA (Sensor 0  data from memory  108 ) may be configured to provide data to StatsPipe 0  (signal  154 ), source Sens 1 DMA (Sensor 1  data from memory  108 ) may be configured to provide data to StatsPipe 1  (signal  156 ), and source ProcInDMA (Sensor 0  and Sensor 1  data from memory  108 ) may be configured to provide data to FEProc (signal  158 ) and FEOut (signal  160 ). 
     It should be noted that the presently illustrated embodiment is configured such that Sens 0 DMA (Sensor 0  frames from memory  108 ) and Sens 1 DMA (Sensor 1  frames from memory  108 ) are only provided to StatsPipe 0  and StatesPipe 1 , respectively. This configuration allows the ISP front-end  80  to retain a certain number of previous frames (e.g., 5 frames) in memory. For example, due to a delay or lag between the time a user initiates a capture event (e.g., transitioning the image system from a preview mode to a capture or a recording mode, or even by just turning on or initializing the image sensor) using the image sensor to when an image scene is captured, not every frame that the user intended to capture may be captured and processed in substantially real-time. Thus, by retaining a certain number of previous frames in memory  108  (e.g., from a preview phase), these previous frames may be processed later or alongside the frames actually captured in response to the capture event, thus compensating for any such lag and providing a more complete set of image data. 
     With regard to the illustrated configuration of  FIG. 8 , it should be noted that the StatsPipe 0   120  is configured to receive one of the inputs  136  (from Sens 0 ),  146  (from Sens 1 ), and  154  (from Sens 0 DMA), as determined by a selection logic  124 , such as a multiplexer. Similarly, selection logic  126  may select an input from the signals  138 ,  156 , and  148  to provide to StatsPipe 1 , and selection logic  132  may select an input from the signals  140 ,  150 , and  158  to provide to FEProc. As mentioned above, the statistical data (Stats 0  and Stats 1 ) may be provided to the control logic  84  for the determination of various control parameters that may be used to operate the imaging device  30  and/or the ISP pipe processing logic  82 . As can be appreciated, the selection logic blocks ( 120 ,  122 , and  132 ) shown in  FIG. 8  may be provided by any suitable type of logic, such as a multiplexer that selects one of multiple input signals in response to a control signal. 
     The pixel processing unit (FEProc)  130  may be configured to perform various image processing operations on the raw image data on a pixel-by-pixel basis. As shown, FEProc  130 , as a destination processing unit, may receive image data from sources Sens 0  (signal  140 ), Sens 1  (signal  150 ), or ProcInDMA (signal  158 ) by way of the selection logic  132 . FEProc  130  may also receive and output various signals (e.g., Rin, Hin, Hout, and Yout—which may represent motion history and luma data used during temporal filtering) when performing the pixel processing operations, which may include temporal filtering and binning compensation filtering, as will be discussed further below. The output  109  (FEProcOut) of the pixel processing unit  130  may then be forwarded to the ISP pipe logic  82 , such as via one or more first-in-first-out (FIFO) queues, or may be sent to the memory  108 . 
     Further, as shown in  FIG. 8 , the selection logic  132 , in addition to receiving the signals  140 ,  150 , and  158 , may further receive the signals  159  and  161 . The signal  159  may represented “pre-processed” raw image data from StatsPipe 0 , and the signal  161  may represent “pre-processed” raw image data from StatsPipe 1 . As will be discussed below, each of the statistics processing units may apply one or more pre-processing operations to the raw image data before collecting statistics. In one embodiment, each of the statistics processing units may perform a degree of defective pixel detection/correction, lens shading correction, black level compensation, and inverse black level compensation. Thus, the signals  159  and  161  may represent raw image data that has been processed using the aforementioned pre-processing operations (as will be discussed in further detail below in  FIG. 37 ). Thus, the selection logic  132  gives the ISP front-end processing logic  80  the flexibility of providing either un-pre-processed raw image data from the Sensor 0  (signal  140 ) and Sensor 1  (signal  150 ) or pre-processed raw image data from StatsPipe 0  (signal  159 ) and StatsPipe 1  (signal  161 ). Additionally, as shown by selection logic units  162  and  163 , the ISP front-end processing logic  80  also has the flexibility of writing either un-pre-processed raw image data from Sensor 0  (signal  134 ) or Sensor 1  (signal  144 ) to the memory  108 , or writing pre-processed raw image data from StatsPipe 0  (signal  159 ) or StatsPipe 1  (signal  161 ) to the memory  108 . 
     To control the operation of the ISP front-end logic  80 , a front-end control unit  164  is provided. The control unit  164  may be configured to initialize and program control registers (referred to herein as “go registers”) for configuring and starting the processing of an image frame and to select an appropriate register bank(s) for updating double-buffered data registers. In some embodiments, the control unit  164  may also provide performance monitoring logic to log clock cycles, memory latency, and quality of service (QOS) information. Further, the control unit  164  may also control dynamic clock gating, which may be used to disable clocks to one or more portions of the ISP front-end  0  when there is not enough data in the input queue from an active sensor. 
     Using the “go registers” mentioned above, the control unit  164  may be able to control the updating of various parameters for each of the processing units (e.g., StatsPipe 0 , StatsPipe 1 , and FEProc) and may interface with the sensor interfaces to control the starting and stopping of the processing units. Generally each of the front-end processing units operates on a frame-by-frame basis. As discussed above (Table 1), the input to the processing units may be from the sensor interface (Sens 0  or Sens 1 ) or from memory  108 . Further, the processing units may utilize various parameters and configuration data, which may be stored in corresponding data registers. In one embodiment, the data registers associated with each processing unit or destination may be grouped into blocks forming a register bank group. In the embodiment of  FIG. 8 , seven register bank groups may be defined in ISP Front-end: SIf 0 , SIf 1 , StatsPipe 0 , StatsPipe 1 , ProcPipe, FEOut and ProcIn. Each register block address space is duplicated to provide two banks of registers. Only the registers that are double buffered are instantiated in the second bank. If a register is not double buffered, the address in the second bank may be mapped to the address of the same register in the first bank. 
     For registers that are double buffered, registers from one bank are active and used by the processing units while the registers from the other bank are shadowed. The shadowed register may be updated by the control unit  164  during the current frame interval while hardware is using the active registers. The determination of which bank to use for a particular processing unit at a particular frame may be specified by a “NextBk” (next bank) field in a go register corresponding to the source providing the image data to the processing unit. Essentially, NextBk is a field that allows the control unit  164  to control which register bank becomes active on a triggering event for the subsequent frame. 
     Before discussing the operation of the go registers in detail,  FIG. 9  provides a general method  166  for processing image data on a frame-by-frame basis in accordance with the present techniques. Beginning at step  168 , the destination processing units targeted by a data source (e.g., Sens 0 , Sens 1 , Sens 0 DMA, Sens 1 DMA, or ProcInDMA) enter an idle state. This may indicate that processing for the current frame is completed and, therefore, the control unit  164  may prepare for processing the next frame. For instance, at step  170 , programmable parameters for each destination processing unit are updated. This may include, for example, updating the NextBk field in the go register corresponding to the source, as well as updating any parameters in the data registers corresponding to the destination units. Thereafter, at step  172 , a triggering event may place the destination units into a run state. Further, as shown at step  174 , each destination unit targeted by the source completes its processing operations for the current frame, and the method  166  may subsequently return to step  168  for the processing of the next frame. 
       FIG. 10  depicts a block diagram view showing two banks of data registers  176  and  178  that may be used by the various destination units of the ISP-front end. For instance, Bank  0  ( 176 ) may include the data registers  1 - n  ( 176   a - 176   d ), and Bank  1  ( 178 ) may include the data registers  1 - n  ( 178   a - 178   d ). As discussed above, the embodiment shown in  FIG. 8  may utilize a register bank (Bank  0 ) having seven register bank groups (e.g., SIf 0 , SIf 1 , StatsPipe 0 , StatsPipe 1 , ProcPipe, FEOut and ProcIn). Thus, in such an embodiment, the register block address space of each register is duplicated to provide a second register bank (Bank  1 ). 
       FIG. 10  also illustrates go register  180  that may correspond to one of the sources. As shown, the go register  180  includes a “NextVld” field  182  and the above-mentioned “NextBk” field  184 . These fields may be programmed prior to starting the processing of the current frame. Particularly, NextVld may indicate the destination(s) to where data from the source is to be sent. As discussed above, NextBk may select a corresponding data register from either Bank 0  or Bank 1  for each destination targeted, as indicated by NextVld. Though not shown in  FIG. 10 , the go register  180  may also include an arming bit, referred to herein as a “go bit,” which may be set to arm the go register. When a triggering event  192  for a current frame is detected, NextVld and NextBk may be copied into a CurrVld field  188  and a CurrBk field  190  of a corresponding current or “active” register  186 . In one embodiment, the current register(s)  186  may be read-only registers that may set by hardware, while remaining inaccessible to software commands within the ISP front-end  80 . 
     As will be appreciated, for each ISP front-end source, a corresponding go register may be provided. For the purposes of this disclosure, the go registers corresponding to the above-discussed sources Sens 0 , Sens 1 , Sens 0 DMA, Sens 1 DMA, and ProcInDMA may be referred to as Sens 0 Go, Sens 1 Go, Sens 0 DMAGo, Sens 1 DMAGo and ProcInDMAGo, respectively. As mentioned above, the control unit may utilize the go registers to control the sequencing of frame processing within the ISP front end  80 . Each go register contains a NextVld field and a NextBk field to indicate what destinations will be valid, and which register bank ( 0  or  1 ) will be used, respectively, for the next frame. When the next frame&#39;s triggering event  192  occurs, the NextVld and NextBk fields are copied to a corresponding active read-only register  186  that indicates the current valid destinations and bank numbers, as shown above in  FIG. 10 . Each source may be configured to operate asynchronously and can send data to any of its valid destinations. Further, it should be understood that for each destination, generally only one source may be active during a current frame. 
     With regard to the arming and triggering of the go register  180 , asserting an arming bit or “go bit” in the go register  180  arms the corresponding source with the associated NextVld and NextBk fields. For triggering, various modes are available depending on whether the source input data is read from memory (e.g., Sens 0 DMA, Sens 1 DMA or ProcInDMA), or whether the source input data is from a sensor interface (e.g., Sens 0  or Sens 1 ). For instance, if the input is from memory  108 , the arming of the go bit itself may serve as the triggering event, since the control unit  164  has control over when data is read from the memory  108 . If the image frames are being input by the sensor interface, then triggering event may depend on the timing at which the corresponding go register is armed relative to when data from the sensor interface is received. In accordance with the present embodiment, three different techniques for triggering timing from a sensor interface input are shown in  FIGS. 11-13 . 
     Referring first to  FIG. 11 , a first scenario is illustrated in which triggering occurs once all destinations targeted by the source transition from a busy or run state to an idle state. Here, a data signal VVALID ( 196 ) represents an image data signal from a source. The pulse  198  represents a current frame of image data, the pulse  202  represents the next frame of image data, and the interval  200  represents a vertical blanking interval (VBLANK)  200  (e.g., represents the time differential between the last line of the current frame  198  and the next frame  202 ). The time differential between the rising edge and falling edge of the pulse  198  represents a frame interval  201 . Thus, in  FIG. 11 , the source may be configured to trigger when all targeted destinations have finished processing operations on the current frame  198  and transition to an idle state. In this scenario, the source is armed (e.g., by setting the arming or “go” bit) before the destinations complete processing so that the source can trigger and initiate processing of the next frame  202  as soon as the targeted destinations go idle. During the vertical blanking interval  200  the processing units may be set up and configured for the next frame  202  using the register banks specified by the go register corresponding to the source before the sensor input data arrives. By way of example only, read buffers used by FEProc  130  may be filled before the next frame  202  arrives. In this case, shadowed registers corresponding to the active register banks may be updated after the triggering event, thus allowing for a full frame interval to setup the double-buffered registers for the next frame (e.g., after frame  202 ). 
       FIG. 12  illustrates a second scenario in which the source is triggered by arming the go bit in the go register corresponding to the source. Under this “trigger-on-go” configuration, the destination units targeted by the source are already idle and the arming of the go bit is the triggering event. This triggering mode may be utilized for registers that are not double-buffered and, therefore, are updated during vertical blanking (e.g., as opposed to updating a double-buffered shadow register during the frame interval  201 ). 
       FIG. 13  illustrates a third triggering mode in which the source is triggered upon detecting the start of the next frame, i.e., a rising VSYNC. However, it should be noted that in this mode, if the go register is armed (by setting the go bit) after the next frame  202  has already started processing, the source will use the target destinations and register banks corresponding to the previous frame, since the CurrVld and CurrBk fields are not updated before the destination start processing. This leaves no vertical blanking interval for setting up the destination processing units and may potentially result in dropped frames, particularly when operating in a dual sensor mode. It should be noted, however, that this mode may nonetheless result in accurate operation if the image processing circuitry  32  is operating in a single sensor mode that uses the same register banks for each frame (e.g., the destination (NextVld) and register banks (NextBk) do not change). 
     Referring now to  FIG. 14 , a control register (or “go register”)  180  is illustrated in more detail. The go register  180  includes the arming “go” bit  204 , as well as the NextVld field  182  and the NextBk field  184 . As discussed above, each source (e.g., Sens 0 , Sens 1 , Sens 0 DMA, Sens 1 DMA, or ProcInDMA) of the ISP front-end  80  may have a corresponding go register  180 . In one embodiment, the go bit  204  may be a single-bit field, and the go register  180  may be armed by setting the go bit  204  to  1 . The NextVld field  182  may contain a number of bits corresponding to the number of destinations in the ISP front-end  80 . For instance, in the embodiment shown in  FIG. 8 , the ISP front-end includes six destinations: Sif 0 DMA, Sif 1 DMA, StatsPipe 0 , StatsPipe 1 , FEProc, and FEOut. Thus, the go register  180  may include six bits in the NextVld field  182 , with one bit corresponding to each destination, and wherein targeted destinations are set to 1. Similarly, the NextBk field  182  may contain a number of bits corresponding to the number of data registers in the ISP front-end  80 . For instance, as discussed above, the embodiment of the ISP front-end  80  shown in  FIG. 8  may include seven data registers: SIf 0 , SIf 1 , StatsPipe 0 , StatsPipe 1 , ProcPipe, FEOut and ProcIn. Accordingly, the NextBk field  184  may include seven bits, with one bit corresponding to each data register, and wherein data registers corresponding to Bank  0  and  1  are selected by setting their respective bit values to 0 or 1, respectively. Thus, using the go register  180 , the source, upon triggering, knows precisely which destination units are to receive frame data, and which register banks are to be used for configuring the targeted destination units. 
     Additionally, due to the dual sensor configuration supported by the ISP circuitry  32 , the ISP front-end may operate in a single sensor configuration mode (e.g., only one sensor is acquiring data) and a dual sensor configuration mode (e.g., both sensors are acquiring data). In a typical single sensor configuration, input data from a sensor interface, such as Sens 0 , is sent to StatsPipe 0  (for statistics processing) and FEProc (for pixel processing). In addition, sensor frames may also be sent to memory (SIf 0 DMA) for future processing, as discussed above. 
     An example of how the NextVld fields corresponding to each source of the ISP front-end  80  may be configured when operating in a single sensor mode is depicted below in Table 2. 
                     TABLE 2                  NextVld per source example: Single sensor mode                                             SIf0   SIf1-                           DMA   DMA   StatsPipe0   StatsPipe1   FEProc   FEOut                                                     Sens0Go   1   X   1   0   1   0       Sens1Go   X   0   0   0   0   0       Sens0DMAGo   X   X   0   X   X   X       Sens1DMAGo   X   X   X   0   X   X       ProcInDMAGo   X   X   X   X   0   0                    
As discussed above with reference to Table 1, the ISP front-end  80  may be configured such that only certain destinations are valid for a particular source. Thus, the destinations in Table 2 marked with “X” are intended to indicate that the ISP front-end  80  is not configured to allow a particular source to send frame data to that destination. For such destinations, the bits of the NextVld field of the particular source corresponding to that destination may always be 0. It should be understood, however, that this is merely one embodiment and, indeed, in other embodiments, the ISP front-end  80  may be configured such that each source is capable of targeting each available destination unit.
 
     The configuration shown above in Table 2 represents a single sensor mode in which only Sensor 0  is providing frame data. For instance, the Sens 0 Go register indicates destinations as being SIf 0 DMA, StatsPipe 0 , and FEProc. Thus, when triggered, each frame of the Sensor 0  image data, is sent to these three destinations. As discussed above, SIf 0 DMA may store frames in memory  108  for later processing, StatsPipe 0  applies statistics processing to determine various statistic data points, and FEProc processes the frame using, for example, temporal filtering and binning compensation filtering. Further, in some configurations where additional statistics are desired (e.g., statistics in different color spaces), StatsPipe 1  may also be enabled (corresponding NextVld set to 1) during the single sensor mode. In such embodiments, the Sensor 0  frame data is sent to both StatsPipe 0  and StatsPipe 1 . Further, as shown in the present embodiment, only a single sensor interface (e.g., Sens 0  or alternatively Sen 0 ) is the only active source during the single sensor mode. 
     With this in mind,  FIG. 15  provides a flow chart depicting a method  206  for processing frame data in the ISP front-end  80  when only a single sensor is active (e.g., Sensor  0 ). While the method  206  illustrates in particular the processing of Sensor 0  frame data by FEProc  130  as an example, it should be understood that this process may be applied to any other source and corresponding destination unit in the ISP front-end  80 . Beginning at step  208 , Sensor 0  begins acquiring image data and sending the captured frames to the ISP front-end  80 . The control unit  164  may initialize programming of the go register corresponding to Sens 0  (the Sensor 0  interface) to determine target destinations (including FEProc) and what bank registers to use, as shown at step  210 . Thereafter, decision logic  212  determines whether a source triggering event has occurred. As discussed above, frame data input from a sensor interface may utilize different triggering modes ( FIGS. 11-13 ). If a trigger event is not detected, the process  206  continues to wait for the trigger. Once triggering occurs, the next frame becomes the current frame and is sent to FEProc (and other target destinations) for processing at step  214 . FEProc may be configured using data parameters based on a corresponding data register (ProcPipe) specified in the NextBk field of the Sens 0 Go register. After processing of the current frame is completed at step  216 , the method  206  may return to step  210 , at which the Sens 0 Go register is programmed for the next frame. 
     When both Sensor 0  and Sensor 1  of the ISP front-end  80  are both active, statistics processing remains generally straightforward, since each sensor input may be processed by a respective statistics block, StatsPipe 0  and StatsPipe 1 . However, because the illustrated embodiment of the ISP front-end  80  provides only a single pixel processing unit (FEProc), FEProc may be configured to alternate between processing frames corresponding to Sensor 0  input data and frames corresponding to Sensor 1  input data. As will be appreciated, the image frames are read from FEProc in the illustrated embodiment to avoid a condition in which image data from one sensor is processed in real-time while image data from the other sensor is not processed in real-time. For instance, as shown in Table 3 below, which depicts one possible configuration of NextVld fields in the go registers for each source when the ISP-front end  80  is operating in a dual sensor mode, input data from each sensor is sent to memory (SIf 0 DMA and SIf 1 DMA) and to the corresponding statistics processing unit (StatsPipe 0  and StatsPipe 1 ). 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 NextVld per source example: Dual sensor mode 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 SIf0- 
                 SIf1- 
                   
                   
                   
                   
               
               
                   
                 DMA 
                 DMA 
                 StatsPipe0 
                 StatsPipe1 
                 FEProc 
                 FEOut 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                 Sens0Go 
                 1 
                 X 
                 1 
                 0 
                 0 
                 0 
               
               
                 Sens1Go 
                 X 
                 1 
                 0 
                 1 
                 0 
                 0 
               
               
                 Sens0DMAGo 
                 X 
                 X 
                 0 
                 X 
                 X 
                 X 
               
               
                 Sens1DMAGo 
                 X 
                 X 
                 X 
                 0 
                 X 
                 X 
               
               
                 ProcInDMAGo 
                 X 
                 X 
                 X 
                 X 
                 1 
                 0 
               
               
                   
               
            
           
         
       
     
     The sensor frames in memory are sent to FEProc from the ProcInDMA source, such that they alternate between Sensor 0  and Sensor 1  at a rate based on their corresponding frame rates. For instance, if Sensor 0  and Sensor 1  are both acquiring image data at a rate of 30 frames per second (fps), then their sensor frames may be interleaved in a 1-to-1 manner. If Sensor 0  (30 fps) is acquiring image data at a rate twice that of Sensor 1  (15 fps), then the interleaving may be 2-to-1, for example. That is, two frames of Sensor 0  data are read out of memory for every one frame of Sensor 1  data. 
     With this in mind,  FIG. 16  depicts a method  220  for processing frame data in the ISP front-end  80  having two sensors acquiring image data simultaneously. At step  222 , both Sensor 0  and Sensor 1  begin acquiring image frames. As will be appreciated, Sensor 0  and Sensor 1  may acquire the image frames using different frame rates, resolutions, and so forth. At step  224 , the acquired frames from Sensor 0  and Sensor 1  written to memory  108  (e.g., using SIf 0 DMA and SIf 1 DMA destinations). Next, source ProcInDMA reads the frame data from the memory  108  in an alternating manner, as indicated at step  226 . As discussed, frames may alternate between Sensor 0  data and Sensor 1  data depending on frame rate at which the data is acquired. At step  228 , the next frame from ProcInDMA is acquired. Thereafter, at step  230 , the NextVld and NextBk fields of the go register corresponding to the source, here ProcInDMA, is programmed depending on whether the next frame is Sensor 0  or Sensor 1  data. Thereafter, decision logic  232  determines whether a source triggering event has occurred. As discussed above, data input from memory may be triggered by arming the go bit (e.g., “trigger-on-go” mode). Thus, triggering may occur once the go bit of the go register is set to 1. Once triggering occurs, the next frame becomes the current frame and is sent to FEProc for processing at step  234 . As discussed above, FEProc may be configured using data parameters based on a corresponding data register (ProcPipe) specified in the NextBk field of the ProcInDMAGo register. After processing of the current frame is completed at step  236 , the method  220  may return to step  228  and continue. 
     A further operational event that the ISP front-end  80  is configured to handle is a configuration change during image processing. For instance, such an event may occur when the ISP front-end  80  transitions from a single sensor configuration to a dual sensor configuration, or vice-versa. As discussed above, the NextVld fields for certain sources may be different depending on whether one or both image sensors are active. Thus, when the sensor configuration is changed, the ISP front-end control unit  164  may release all destination units before they are targeted by a new source. This may avoid invalid configurations (e.g., assigning multiple sources to one destination). In one embodiment, the release of the destination units may be accomplished by setting the NextVld fields of all the go registers to 0, thus disabling all destinations, and arming the go bit. After the destination units are released, the go registers may be reconfigured depending on the current sensor mode, and image processing may continue. 
     A method  240  for switching between single and dual sensor configurations is shown in  FIG. 17 , in accordance with one embodiment. Beginning at step  242 , a next frame of image data from a particular source of the ISP front-end  80  is identified. At step  244 , the target destinations (NextVld) are programmed into the go register corresponding to the source. Next, at step  246 , depending on the target destinations, NextBk is programmed to point to the correct data registers associated with the target destinations. Thereafter, decision logic  248  determines whether a source triggering event has occurred. Once triggering occurs, the next frame is sent to the destination units specified by NextVld and processed by the destination units using the corresponding data registers specified by NextBk, as shown at step  250 . The processing continues until step  252 , at which the processing of the current frame is completed. 
     Subsequently, decision logic  254  determines whether there is a change in the target destinations for the source. As discussed above, NextVld settings of the go registers corresponding to Sens 0  and Sens 1  may vary depending on whether one sensor or two sensors are active. For instance, referring to Table 2, if only Sensor 0  is active, Sensor 0  data is sent to SIf 0 DMA, StatsPipe 0 , and FEProc. However, referring to Table 3, if both Sensor 0  and Sensor 1  are active, then Sensor 0  data is not sent directly FEProc. Instead, as mentioned above, Sensor 0  and Sensor 1  data is written to memory  108  and is read out to FEProc in an alternating manner by source ProcInDMA. Thus, if no target destination change is detected at decision logic  254 , the control unit  164  deduces that the sensor configuration has not changed, and the method  240  returns to step  246 , whereat the NextBk field of the source go register is programmed to point to the correct data registers for the next frame, and continues. 
     If, however, at decision logic  254 , a destination change is detected, then the control unit  164  determines that a sensor configuration change has occurred. For instance, this could represent switching from single sensor mode to dual sensor mode, or shutting off the sensors altogether. Accordingly, the method  240  continues to step  256 , at which all bits of the NextVld fields for all go registers are set to 0, thus effectively disabling the sending of frames to any destination on the next trigger. Then, at decision logic  258 , a determination is made as to whether all destination units have transition to an idle state. If not, the method  240  waits at decision logic  258  until all destinations units have completed their current operations. Next, at decision logic  260 , a determination is made as to whether image processing is to continue. For instance, if the destination change represented the deactivation of both Sensor 0  and Sensor 1 , then image processing ends at step  262 . However, if it is determined that image processing is to continue, then the method  240  returns to step  244  and the NextVld fields of the go registers are programmed in accordance with the current operation mode (e.g., single sensor or dual sensor). As shown here, the steps  254 - 262  for clearing the go registers and destination fields may collectively be referred to by reference number  264 . 
     Next,  FIG. 18  shows a further embodiment by way of the flow chart (method  265 ) that provides for another dual sensor mode of operation. The method  265  depicts a condition in which one sensor (e.g., Sensor 0 ) is actively acquiring image data and sending the image frames to FEProc  130  for processing, while also sending the image frames to StatsPipe 0  and/or memory  108  (Sif 0 DMA), while the other sensor (e.g., Sensor 1 ) is inactive (e.g., turned off), as shown at step  266 . Decision logic  268  then detects for a condition in which Sensor 1  will become active on the next frame to send image data to FEProc. If this condition is not met, then the method  265  returns to step  266 . However, if this condition is met, then the method  265  proceeds by performing action  264  (collectively steps  254 - 262  of  FIG. 17 ), whereby the destination fields of the sources are cleared and reconfigured at step  264 . For instance, at step  264 , the NextVld field of the go register associated with Sensor 1  may be programmed to specify FEProc as a destination, as well as StatsPipe 1  and/or memory (Sif 1 DMA), while the NextVld field of the go register associated with Sensor 0  may be programmed to clear FEProc as a destination. In this embodiment, although frames captured by Sensor 0  are not sent to FEProc on the next frame, Sensor 0  may remain active and continue to send its image frames to StatsPipe 0 , as shown at step  270 , while Sensor 1  captures and sends data to FEProc for processing at step  272 . Thus, both sensors, Sensor 0  and Sensor 1  may continue to operate in this “dual sensor” mode, although only image frames from one sensor are sent to FEProc for processing. For the purposes of this example, a sensor sending frames to FEProc for processing may be referred to as an “active sensor,” a sensor that is not sending frame FEProc but is still sending data to the statistics processing units may be referred to as a “semi-active sensor,” and a sensor that is not acquiring data at all may be referred to as an “inactive sensor.” 
     One benefit of the foregoing technique is that the because statistics continue to be acquired for the semi-active sensor (Sensor 0 ), the next time the semi-active sensor transitions to an active state and the current active sensor (Sensor 1 ) transitions to a semi-active or inactive state, the semi-active sensor may begin acquiring data within one frame, since color balance and exposure parameters may already be available due to the continued collection of image statistics. This technique may be referred to as “hot switching” of the image sensors, and avoids drawbacks associated with “cold starts” of the image sensors (e.g., starting with no statistics information available). Further, to save power, since each source is asynchronous (as mentioned above), the semi-active sensor may operate at a reduced clock and/or frame rate during the semi-active period. 
     Before continuing with a more detailed description of the statistics processing and pixel processing operations depicted in the ISP front-end logic  80  of  FIG. 8 , it is believed that a brief introduction regarding the definitions of various ISP frame regions will help to facilitate a better understanding of the present subject matter. With this in mind, various frame regions that may be defined within an image source frame are illustrated in  FIG. 19 . The format for a source frame provided to the image processing circuitry  32  may use either the tiled or linear addressing modes discussed above, as may utilize pixel formats in 8, 10, 12, or 14-bit precision. The image source frame  274 , as shown in  FIG. 19 , may include a sensor frame region  276 , a raw frame region  276 , and an active region  278 . The sensor frame  276  is generally the maximum frame size that the image sensor  90  can provide to the image processing circuitry  32 . The raw frame region  278  may be defined as the region of the sensor frame  276  that is sent to the ISP front-end processing logic  80 . The active region  280  may be defined as a portion of the source frame  274 , typically within the raw frame region  278 , on which processing is performed for a particular image processing operation. In accordance with embodiments of the present technique, that active region  280  may be the same or may be different for different image processing operations. 
     In accordance with aspects of the present technique, the ISP front-end logic  80  only receives the raw frame  278 . Thus, for the purposes of the present discussion, the global frame size for the ISP front-end processing logic  80  may be assumed as the raw frame size, as determined by the width  282  and height  284 . In some embodiments, the offset from the boundaries of the sensor frame  276  to the raw frame  278  may be determined and/or maintained by the control logic  84 . For instance, the control logic  84  may be include firmware that may determine the raw frame region  278  based upon input parameters, such as the x-offset  286  and the y-offset  288 , that are specified relative to the sensor frame  276 . Further, in some cases, a processing unit within the ISP front-end logic  80  or the ISP pipe logic  82  may have a defined active region, such that pixels in the raw frame but outside the active region  280  will not be processed, i.e., left unchanged. For instance, an active region  280  for a particular processing unit having a width  290  and height  292  may be defined based upon an x-offset  294  and y-offset  296  relative to the raw frame  278 . Further, where an active region is not specifically defined, one embodiment of the image processing circuitry  32  may assume that the active region  280  is the same as the raw frame  278  (e.g., x-offset  294  and y-offset  296  are both equal to 0). Thus, for the purposes of image processing operations performed on the image data, boundary conditions may be defined with respect to the boundaries of the raw frame  278  or active region  280 . 
     Keeping these points in mind and referring to  FIG. 20 , a more detailed view of the ISP front-end pixel processing logic  130  (previously discussed in  FIG. 8 ) is illustrated, in accordance with an embodiment of the present technique. As shown, the ISP front-end pixel processing logic  130  includes a temporal filter  298  and a binning compensation filter  300 . The temporal filter  298  may receive one of the input image signals Sif 0 , Sif 1 , FEProcIn, or pre-processed image signals (e.g.,  159 ,  161 ) and may operate on the raw pixel data before any additional processing is performed. For example, the temporal filter  298  may initially process the image data to reduce noise by averaging image frames in the temporal direction. The binning compensation filter  300 , which is discussed in more detail below, may apply scaling and re-sampling on binned raw image data from an image sensor (e.g.,  90   a ,  90   b ) to maintain an even spatial distribution of the image pixels. 
     The temporal filter  298  may be pixel-adaptive based upon motion and brightness characteristics. For instance, when pixel motion is high, the filtering strength may be reduced in order to avoid the appearance of “trailing” or “ghosting artifacts” in the resulting processed image, whereas the filtering strength may be increased when little or no motion is detected. Additionally, the filtering strength may also be adjusted based upon brightness data (e.g., “luma”). For instance, as image brightness increases, filtering artifacts may become more noticeable to the human eye. Thus, the filtering strength may be further reduced when a pixel has a high level of brightness. 
     In applying temporal filtering, the temporal filter  298  may receive reference pixel data (Rin) and motion history input data (Hin), which may be from a previous filtered or original frame. Using these parameters, the temporal filter  298  may provide motion history output data (Hout) and filtered pixel output (Yout). The filtered pixel output Yout is then passed to the binning compensation filter  300 , which may be configured to perform one or more scaling operations on the filtered pixel output data Yout to produce the output signal FEProcOut. The processed pixel data FEProcOut may then be forwarded to the ISP pipe processing logic  82 , as discussed above. 
     Referring to  FIG. 21 , a process diagram depicting a temporal filtering process  302  that may be performed by the temporal filter shown in  FIG. 20  is illustrated, in accordance with a first embodiment. The temporal filter  298  may include a 2-tap filter, wherein the filter coefficients are adjusted adaptively on a per pixel basis based at least partially upon motion and brightness data. For instance, input pixels x(t), with the variable “t” denoting a temporal value, may be compared to reference pixels r(t−1) in a previously filtered frame or a previous original frame to generate a motion index lookup in a motion history table (M)  304  that may contain filter coefficients. Additionally, based upon motion history input data h(t−1), a motion history output h(t) corresponding to the current input pixel x(t) may be determined. 
     The motion history output h(t) and a filter coefficient, K, may be determined based upon a motion delta d(j,i,t), wherein (j,i) represent coordinates of the spatial location of a current pixel x(j,i,t). The motion delta d(j,i,t) may be computed by determining the maximum of three absolute deltas between original and reference pixels for three horizontally collocated pixels of the same color. For instance, referring briefly to  FIG. 22 , the spatial locations of three collocated reference pixels  308 ,  309 , and  310  that corresponding to original input pixels  312 ,  313 , and  314  are illustrated. In one embodiment, the motion delta may be calculated based on these original and reference pixels using formula below:
 
 d ( j,i,t )=max 3[abs( x ( j,i− 2, t )− r ( j,i− 2, t− 1)),
 
(abs( x ( j,i,t )− r ( j,i,t− 1)),
 
(abs( x ( j,i+ 2, t )− r ( j,i+ 2, t− 1))]  (1a)
 
A flow chart depicting this technique for determining the motion delta value is illustrated further below in  FIG. 24 . Further, it should be understood that the technique for calculating the motion delta value, as shown above in Equation 1a (and below in  FIG. 24 ), is only intended to provide one embodiment for determining a motion delta value.
 
     In other embodiments, an array of same-colored pixels could be evaluated to determine a motion delta value. For instance, in addition to the three pixels referenced in Equation 1a, one embodiment for determining motion delta values may include also evaluating the absolute deltas between same colored pixels from two rows above (e.g., j−2; assuming a Bayer pattern) the reference pixels  312 ,  313 , and  314  and their corresponding collocated pixels, and two rows below (e.g., j+2; assuming a Bayer pattern) the reference pixels  312 ,  313 , and  314  and their corresponding collocated pixels. For instance, in one embodiment, the motion delta value may be expressed as follows:
 
 d ( j,i,t )=max 9[abs( x ( j,i− 2, t )− r ( j,i− 2, t− 1)),
 
(abs( x ( j,i,t )− r ( j,i,t− 1)),
 
(abs( x ( j,i+ 2, t )− r ( j,i+ 2, t− 1)),
 
(abs( x ( j− 2, i− 2, t )− r ( j− 2, i− 2, t− 1)),
 
(abs( x ( j− 2, i,t )− r ( j− 2, i,t− 1)),
 
(abs( x ( j− 2, i+ 2, t )− r ( j− 2, i+ 2, t− 1)),
 
(abs( x ( j+ 2, i− 2, t )− r ( j+ 2, i− 2, t− 1))
 
(abs( x ( j+ 2, i,t )− r ( j+ 2, i,t− 1)),
 
(abs( x ( j+ 2, i+ 2, t )− r ( j+ 2, i+ 2, t− 1))]  (1b)
 
Thus, in the embodiment depicted by Equation 1b, the motion delta value may be determined by comparing the absolute delta between a 3×3 array of same-colored pixels, with the current pixel ( 313 ) being located at the center of the 3×3 array (e.g., really a 5×5 array for Bayer color patterns if pixels of different colors are counted). It should be appreciated, that any suitable two-dimensional array of same-colored pixels (e.g., including arrays having all pixels in the same row (e.g., Equation 1a) or arrays having all pixels in the same column) with the current pixel (e.g.,  313 ) being located at the center of the array could be analyzed to determine a motion delta value. Further, while the motion delta value could be determined as the maximum of the absolute deltas (e.g., as shown in Equations 1a and 1b), in other embodiments, the motion delta value could also be selected as the mean or median of the absolute deltas. Additionally, the foregoing techniques may also be applied to other types of color filter arrays (e.g., RGBW, CYGM, etc.), and is not intended to be exclusive to Bayer patterns.
 
     Referring back to  FIG. 21 , once the motion delta value is determined, a motion index lookup that may be used to selected the filter coefficient K from the motion table (M)  304  may be calculated by summing the motion delta d(t) for the current pixel (e.g., at spatial location (j,i)) with the motion history input h(t−1). For instance, the filter coefficient K may be determined as follows:
 
 K=M[d ( j,i,t )+ h ( j,i,t− 1)]  (2a)
 
Additionally, the motion history output h(t) may be determined using the following formula:
 
 h ( j,i,t )= d ( j,i,t )+(1− K )× h ( j,i,t− 1)  (3a)
 
     Next, the brightness of the current input pixel x(t) may be used to generate a luma index lookup in a luma table (L)  306 . In one embodiment, the luma table may contain attenuation factors that may be between 0 and 1, and may be selected based upon the luma index. A second filter coefficient, K′, may be calculated by multiplying the first filter coefficient K by the luma attenuation factor, as shown in the following equation:
 
 K′=K×L[x ( j,i,t )]  (4a)
 
     The determined value for K′ may then be used as the filtering coefficient for the temporal filter  298 . As discussed above, the temporal filter  298  may be a 2-tap filter. Additionally, the temporal filter  298  may be configured as an infinite impulse response (IIR) filter using previous filtered frame or as a finite impulse response (FIR) filter using previous original frame. The temporal filter  298  may compute the filtered output pixel y(t) (Yout) using the current input pixel x(t), the reference pixel r(t−1), and the filter coefficient K′ using the following formula:
 
 y ( j,i,t )= r ( j,i,t− 1)+ K ′( x ( j,i,t )− r ( j,i,t− 1))  (5a)
 
As discussed above, the temporal filtering process  302  shown in  FIG. 21  may be performed on a pixel-by-pixel basis. In one embodiment, the same motion table M and luma table L may be used for all color components (e.g., R, G, and B). Additionally, some embodiments may provide a bypass mechanism, in which temporal filtering may be bypassed, such as in response to a control signal from the control logic  84 . Further, as will be discussed below with respect to  FIGS. 26 and 27 , one embodiment of the temporal filter  298  may utilize separate motion and luma tables for each color component of the image data.
 
     The embodiment of the temporal filtering technique described with reference to  FIGS. 21 and 22  may be better understood in view of  FIG. 23 , which depicts a flow chart illustrating a method  315 , in accordance with the above-described embodiment. The method  315  begins at step  316 , at which a current pixel x(t) located at spatial location (j,i) of a current frame of image data is received by the temporal filtering system  302 . At step  317 , a motion delta value d(t) is determined for the current pixel x(t) based at least partially upon one or more collocated reference pixels (e.g., r(t−1)) from a previous frame of the image data (e.g., the image frame immediately preceding the current frame). A technique for determining a motion delta value d(t) at step  317  is further explained below with reference to  FIG. 24 , and may be performed in accordance with Equation 1a, as shown above. 
     Once the motion delta value d(t) from step  317  is obtained, a motion table lookup index may be determined using the motion delta value d(t) and a motion history input value h(t−1) corresponding to the spatial location (j,i) from the previous frame, as shown in step  318 . Additionally, though not shown, a motion history value h(t) corresponding to the current pixel x(t) may also be determined at step  318  once the motion delta value d(t) is known, for example, by using Equation 3a shown above. Thereafter, at step  319 , a first filter coefficient K may be selected from a motion table  304  using the motion table lookup index from step  318 . The determination of the motion table lookup index and the selection of the first filter coefficient K from the motion table may be performed in accordance with Equation 2a, as shown above. 
     Next, at step  320 , an attenuation factor may be selected from a luma table  306 . For instance, the luma table  306  may contain attenuation factors ranging from between approximately 0 and 1, and the attenuation factor may be selected from the luma table  306  using the value of the current pixel x(t) as a lookup index. Once the attenuation factor is selected, a second filter coefficient K′ may be determined at step  321  using the selected attenuation factor and the first filter coefficient K (from step  319 ), as shown in Equation 4a above. Then, at step  322 , a temporally filtered output value y(t) corresponding to the current input pixel x(t) is determined based upon the second filter coefficient K′ (from step  320 ), the value of the collocated reference pixel r(t−1), and the value of the input pixel x(t). For instance, in one embodiment, the output value y(t) may be determined in accordance with Equation 5a, as shown above. 
     Referring to  FIG. 24 , the step  317  for determining the motion delta value d(t) from the method  315  is illustrated in more detail in accordance with one embodiment. In particular, the determination of the motion delta value d(t) may generally correspond to the operation depicted above in accordance with Equation 1a. As shown, the step  317  may include the sub-steps  324 - 327 . Beginning at sub-step  324 , a set of three horizontally adjacent pixels having the same color value as the current input pixel x(t) are identified. By way of example, in accordance with the embodiment shown in  FIG. 22  the image data may include Bayer image data, and the three horizontally adjacent pixels may include the current input pixel x(t) ( 313 ), a second pixel  312  of the same color to the left of the current input pixel  313 , and a third pixel of the same color to the right of the current input pixel  313 . 
     Next, at sub-step  325 , three collocated reference pixels  308 ,  309 , and  310  from the previous frame corresponding to the selected set of three horizontally adjacent pixels  312 ,  313 , and  314  are identified. Using the selected pixels  312 ,  313 , and  314  and the three collocated reference pixels  308 ,  309 , and  310 , the absolute values of the differences between each of the three selected pixels  312 ,  313 , and  314  and their corresponding collocated reference pixels  308 ,  309 , and  310 , respectively, are determined at sub-step  326 . Subsequently, at sub-step  327 , the maximum of the three differences from sub-step  326  is selected as the motion delta value d(t) for the current input pixel x(t). As discussed above,  FIG. 24 , which illustrates the motion delta value calculation technique shown in Equation 1a, is only intended to provide one embodiment. Indeed, as discussed above, any suitable two-dimensional array of same-colored pixels with the current pixel being centered in the array may be used to determine a motion delta value (e.g., Equation 1b). 
     Another embodiment of a technique for applying temporal filtering to image data is further depicted in  FIG. 25 . For instance, since signal to noise ratios for different color components of the image data may be different, a gain may be applied to the current pixel, such that the current pixel is gained before selecting motion and luma values from the motion table  304  and luma table  306 . By applying a respective gain that is color dependent, signal to noise ratio may be more consistent among the different color components. By way of example only, in an implementation that uses raw Bayer image data, the red and blue color channels may generally be more sensitive compared to the green (Gr and Gb) color channels. Thus, by applying an appropriate color-dependent gain to each processed pixel, the signal to noise variation between each color component may be generally reduced, thereby reducing, among other things, ghosting artifacts, as well as consistency across different colors after auto-white balance gains. 
     With this in mind,  FIG. 25  provides a flow chart depicting a method  328  for applying temporal filtering to image data received by the front-end processing unit  130  in accordance with such an embodiment. Beginning at step  329 , a current pixel x(t) located at spatial location (j,i) of a current frame of image data is received by the temporal filtering system  302 . At step  330 , a motion delta value d(t) is determined for the current pixel x(t) based at least partially upon one or more collocated reference pixels (e.g., r(t−1)) from a previous frame of the image data (e.g., the image frame immediately preceding the current frame). The step  330  may be similar to the step  317  of  FIG. 23 , and may utilize the operation represented in Equation 1 above. 
     Next, at step  331 , a motion table lookup index may be determined using the motion delta value d(t), a motion history input value h(t−1) corresponding to the spatial location (j,i) from the previous frame (e.g., corresponding to the collocated reference pixel r(t−1)), and a gain associated with the color of the current pixel. Thereafter, at step  332 , a first filter coefficient K may be selected from the motion table  304  using the motion table lookup index determined at step  331 . By way of example only, in one embodiment, the filter coefficient K and the motion table lookup index may be determined as follows:
 
 K=M [gain[ c ]×( d ( j,i,t )+ h ( j,i,t− 1))],  (2b)
 
wherein M represents the motion table, and wherein the gain[c] corresponds to a gain associated with the color of the current pixel. Additionally, though not shown in  FIG. 25 , it should be understood that a motion history output value h(t) for the current pixel may also be determined and may be used to apply temporal filtering to a collocated pixel of a subsequent image frame (e.g., the next frame). In the present embodiment, the motion history output h(t) for the current pixel x(t) may be determined using the following formula:
 
 h ( j,i,t )= d ( j,i,t )+ K[h ( j,i,t− 1)− d ( j,i,t )]  (3b)
 
     Next, at step  333 , an attenuation factor may be selected from the luma table  306  using a luma table lookup index determined based upon the gain (gain[c]) associated with the color of the current pixel x(t). As discussed above, the attenuation factors stored in the luma table may have a range from approximately 0 to 1. Thereafter, at step  334 , a second filter coefficient K′ may be calculated based upon the attenuation factor (from step  333 ) and the first filter coefficient K (from step  332 ). By way of example only, in one embodiment, the second filter coefficient K′ and the luma table lookup index may be determined as follows:
 
 K′=K×L [gain[ c]×x ( j,i,t )]  (4b)
 
     Next, at step  335 , a temporally filtered output value y(t) corresponding to the current input pixel x(t) is determined based upon the second filter coefficient K′ (from step  334 ), the value of the collocated reference pixel r(t−1), and the value of the input pixel x(t). For instance, in one embodiment, the output value y(t) may be determined as follows:
 
 y ( j,i,t )= x ( j,i,t )+ K ′( r ( j,i,t− 1)− x ( j,i,t ))  (5b)
 
     Continuing to  FIG. 26 , a further embodiment of the temporal filtering process  336  is depicted. Here, the temporal filtering process  336  may be accomplished in a manner similar to the embodiment discussed in  FIG. 25 , except that instead of applying a color-dependent gain (e.g., gain[c]) to each input pixel and using shared motion and luma tables, separate motion and luma tables are provided for each color components. For instance, as shown in  FIG. 26 , the motion tables  304  may include a motion table  304   a  corresponding to a first color, a motion table  304   b  corresponding to a second color, and a motion table  304   c  corresponding to an nth color, wherein n depends on the number of colors present in the raw image data. Similarly, the luma tables  306  may include a luma table  306   a  corresponding to the first color, a luma table  306   b  corresponding to the second color, and the motion table  304   c  corresponding to the nth color. Thus, in an embodiment where the raw image data is Bayer image data, three motion and luma tables may be provided for each of the red, blue, and green color components. As discussed below, the selection of filtering coefficients K and attenuation factors may depend on the motion and luma table selected for the current color (e.g., the color of the current input pixel). 
     A method  338  illustrating a further embodiment for temporal filtering using color-dependent motion and luma tables is shown in  FIG. 27 . As will be appreciated, the various calculations and formulas that may be employed by the method  338  may be similar to the embodiment shown in  FIG. 23 , but with a particular motion and luma table being selected for each color, or similar to the embodiment shown in  FIG. 25 , but replacing the use of the color dependent gain[c] with the selection of a color-dependent motion and luma table. 
     Beginning at step  339 , a current pixel x(t) located at spatial location (j,i) of a current frame of image data is received by the temporal filtering system  336  ( FIG. 26 ). At step  340 , a motion delta value d(t) is determined for the current pixel x(t) based at least partially upon one or more collocated reference pixels (e.g., r(t−1)) from a previous frame of the image data (e.g., the image frame immediately preceding the current frame). Step  340  may be similar to the step  317  of  FIG. 23 , and may utilize the operation shown in Equation 1 above. 
     Next, at step  341 , a motion table lookup index may be determined using the motion delta value d(t) and a motion history input value h(t−1) corresponding to the spatial location (j,i) from the previous frame (e.g., corresponding to the collocated reference pixel r(t−1)). Thereafter, at step  342 , a first filter coefficient K may be selected from one of the available motion tables (e.g.,  304   a ,  304   b ,  304   c ) based upon the color of the current input pixel. For instance, one the appropriate motion table is identified, the first filter coefficient K may be selected using the motion table lookup index determined in step  341 . 
     After selecting the first filter coefficient K, a luma table corresponding to the current color is selected and an attenuation factor is selected from the selected luma table based upon the value of the current pixel x(t), as shown at step  343 . Thereafter, at step  344 , a second filter coefficient K′ is determined based upon the attenuation factor (from step  343 ) and the first filter coefficient K (step  342 ). Next, at step  345 , a temporally filtered output value y(t) corresponding to the current input pixel x(t) is determined based upon the second filter coefficient K′ (from step  344 ), the value of the collocated reference pixel r(t−1), and the value of the input pixel x(t). While the technique shown in  FIG. 27  may be more costly to implement (e.g., due to the memory needed for storing additional motion and luma tables), it may, in some instances, offer further improvements with regard to ghosting artifacts and consistency across different colors after auto-white balance gains. 
     In accordance with further embodiments, the temporal filtering process provided by the temporal filter  298  may utilize a combination of color-dependent gains and color-specific motion and/or luma tables for applying temporal filtering to the input pixels. For instance, in one such embodiment, a single motion table may be provided for all color components, and the motion table lookup index for selecting the first filtering coefficient (K) from the motion table may be determined based upon a color dependent gain (e.g., as shown in  FIG. 25 , steps  331 - 332 ), while the luma table lookup index may not have a color dependent gain applied thereto, but may be used to select the brightness attenuation factor from one of multiple luma tables depending upon the color of the current input pixel (e.g., as shown in  FIG. 27 , step  343 ). Alternatively, in another embodiment, multiple motion tables may be provided and a motion table lookup index (without a color dependent gain applied) may be used to select the first filtering coefficient (K) from a motion table corresponding to the color of the current input pixel (e.g., as shown in  FIG. 27 , step  342 ), while a single luma table may be provided for all color components, and wherein the luma table lookup index for selecting the brightness attenuation factor may be determined based upon a color dependent gain (e.g., as shown in  FIG. 25 , steps  333 - 334 ). Further, in one embodiment where a Bayer color filter array is utilized, one motion table and/or luma table may be provided for each of the red (R) and blue (B) color components, while a common motion table and/or luma table may be provided for both green color components (Gr and Gb). 
     The output of the temporal filter  298  may subsequently be sent to the binning compensation filter (BCF)  300 , which may be configured to process the image pixels to compensate for non-linear placement (e.g., uneven spatial distribution) of the color samples due to binning by the image sensor(s)  90   a  or  90   b , such that subsequent image processing operations in the ISP pipe logic  82  (e.g., demosaicing, etc.) that depend on linear placement of the color samples can operate correctly. For example, referring now to  FIG. 28 , a full resolution sample  346  of Bayer image data is depicted. This may represent a full resolution sample raw image data captured by the image sensor  90   a  (or  90   b ) coupled to the ISP front-end processing logic  80 . 
     As will be appreciated, under certain image capture conditions, it may be not be practical to send the full resolution image data captured by the image sensor  90   a  to the ISP circuitry  32  for processing. For instance, when capturing video data, in order to preserve the appearance of a fluid moving image from the perspective of the human eye, a frame rate of at least approximately 30 frames per second may be desired. However, if the amount of pixel data contained in each frame of a full resolution sample exceeds the processing capabilities of the ISP circuitry  32  when sampled at 30 frames per second, binning compensation filtering may be applied in conjunction with binning by the image sensor  90   a  to reduce the resolution of the image signal while also improving signal-to-noise ratio. For instance, as discussed above, various binning techniques, such as 2×2 binning, may be applied to produce a “binned” raw image pixel by averaging the values of surrounding pixels in the active region  280  of the raw frame  278 . 
     Referring to  FIG. 29 , an embodiment of the image sensor  90   a  that may be configured to bin the full resolution image data  346  of  FIG. 28  to produce corresponding binned raw image data  358  shown in  FIG. 30  is illustrated in accordance with one embodiment. As shown, the image sensor  90   a  may capture the full resolution raw image data  346 . Binning logic  357  may be configured to apply binning to the full resolution raw image data  346  to produce the binned raw image data  358 , which may be provided to the ISP front-end processing logic  80  using the sensor interface  94   a  which, as discussed above, may be an SMIA interface or any other suitable parallel or serial camera interfaces. 
     As illustrated in  FIG. 30 , the binning logic  357  may apply 2×2 binning to the full resolution raw image data  346 . For example, with regard to the binned image data  358 , the pixels  350 ,  352 ,  354 , and  356  may form a Bayer pattern and may be determined by averaging the values of the pixels from the full resolution raw image data  346 . For instance, referring to both  FIGS. 28 and 30 , the binned Gr pixel  350  may be determined as the average or mean of the full resolution Gr pixels  350   a - 350   d . Similarly, the binned R pixel  352  may be determined as the average of the full resolution R pixels  352   a - 352   d , the binned B pixel  354  may be determined as the average of the full resolution B pixels  354   a - 354   d , and the binned Gb pixel  356  may be determined as the average of the full resolution Gb pixels  356   a - 356   d . Thus, in the present embodiment, 2×2 binning may provide a set of four full resolution pixels including an upper left (e.g.,  350   a ), upper right (e.g.,  350   b ), lower left (e.g.,  350   c ), and lower right (e.g.,  350   d ) pixel that are averaged to derive a binned pixel located at the center of a square formed by the set of four full resolution pixels. Accordingly, the binned Bayer block  348  shown in  FIG. 30  contains four “superpixels” that represent the 16 pixels contained in the Bayer blocks  348   a - 348   d  of  FIG. 28 . 
     In addition to reducing spatial resolution, binning also offers the added advantage of reducing noise in the image signal. For instance, whenever an image sensor (e.g.,  90   a ) is exposed to a light signal, there may be a certain amount of noise, such as photon noise, associated with the image. This noise may be random or systematic and it also may come from multiple sources. Thus, the amount of information contained in an image captured by the image sensor may be expressed in terms of a signal-to-noise ratio. For example, every time an image is captured by an image sensor  90   a  and transferred to a processing circuit, such as the ISP circuitry  32 , there may be some degree of noise in the pixels values because the process of reading and transferring the image data inherently introduces “read noise” into the image signal. This “read noise” may be random and is generally unavoidable. By using the average of four pixels, noise, (e.g., photon noise) may generally be reduced irrespective of the source of the noise. 
     Thus, when considering the full resolution image data  346  of  FIG. 28 , each Bayer pattern (2×2 block)  348   a - 348   d  contains 4 pixels, each of which contains a signal and noise component. If each pixel in, for example, the Bayer block  348   a , is read separately, then four signal components and four noise components are present. However, by applying binning, as shown in  FIGS. 28 and 30 , such that four pixels (e.g.,  350   a ,  350   b ,  350   c ,  350   d ) may be represented by a single pixel (e.g.,  350 ) in the binned image data, the same area occupied by the four pixels in the full resolution image data  346  may be read as a single pixel with only one instance of a noise component, thus improving signal-to-noise ratio. 
     Further, while the present embodiment depicts the binning logic  357  of  FIG. 29  as being configured to apply a 2×2 binning process, it should be appreciated that the binning logic  357  may be configured to apply any suitable type of binning process, such as 3×3 binning, vertical binning, horizontal binning, and so forth. In some embodiments, the image sensor  90   a  may be configured to select between different binning modes during the image capture process. Additionally, in further embodiments, the image sensor  90   a  may also be configured to apply a technique that may be referred to as “skipping,” wherein instead of average pixel samples, the logic  357  selects only certain pixels from the full resolution data  346  (e.g., every other pixel, every 3 pixels, etc.) to output to the ISP front-end  80  for processing. Further, while only the image sensor  90   a  is shown in  FIG. 29 , it should be appreciated that the image sensor  90   b  may be implemented in a similar manner. 
     As also depicted in  FIG. 30 , one effect of the binning process is that the spatial sampling of the binned pixels may not be equally spaced. This spatial distortion may, in some systems, result in aliasing (e.g., jagged edges), which is generally not desirable. Further, because certain image processing steps in the ISP pipe logic  82  may depend upon on the linear placement of the color samples in order to operate correctly, the binning compensation filter (BCF)  300  may be applied to perform re-sampling and re-positioning of the binned pixels such that the binned pixels are spatially evenly distributed. That is, the BCF  300  essentially compensates for the uneven spatial distribution (e.g., shown in  FIG. 30 ) by re-sampling the position of the samples (e.g., pixels). For instance,  FIG. 31  illustrates a re-sampled portion of binned image data  360  after being processed by the BCF  300 , wherein the Bayer block  361  containing the evenly distributed re-sampled pixels  362 ,  363 ,  364 , and  365  correspond to the binned pixels  350 ,  352 ,  354 , and  356 , respectively, of the binned image data  358  from  FIG. 30 . Additionally, in an embodiment that utilizes skipping (e.g., instead of binning), as mentioned above, the spatial distortion shown in  FIG. 30  may not be present. In this case, the BCF  300  may function as a low pass filter to reduce artifacts (e.g., aliasing) that may result when skipping is employed by the image sensor  90   a.    
       FIG. 32  shows a block diagram of the binning compensation filter  300  in accordance with one embodiment. The BCF  300  may include binning compensation logic  366  that may process binned pixels  358  to apply horizontal and vertical scaling using horizontal scaling logic  368  and vertical scaling logic  370 , respectively, to re-sample and re-position the binned pixels  358  so that they are arranged in a spatially even distribution, as shown in  FIG. 31 . In one embodiment, the scaling operation(s) performed by the BCF  300  may be performed using horizontal and vertical multi-tap polyphase filtering. For instance, the filtering process may include selecting the appropriate pixels from the input source image data (e.g., the binned image data  358  provided by the image sensor  90   a ), multiplying each of the selected pixels by a filtering coefficient, and summing up the resulting values to form an output pixel at a desired destination. 
     The selection of the pixels used in the scaling operations, which may include a center pixel and surrounding neighbor pixels of the same color, may be determined using separate differential analyzers  372 , one for vertical scaling and one for horizontal scaling. In the depicted embodiment, the differential analyzers  372  may be digital differential analyzers (DDAs) and may be configured to control the current output pixel position during the scaling operations in the vertical and horizontal directions. In the present embodiment, a first DDA (referred to as  372   a ) is used for all color components during horizontal scaling, and a second DDA (referred to as  372   b ) is used for all color components during vertical scaling. By way of example only, the DDA  372  may be provided as a 32-bit data register that contains a 2&#39;s-complement fixed-point number having 16 bits in the integer portion and 16 bits in the fraction. The 16-bit integer portion may be used to determine the current position for an output pixel. The fractional portion of the DDA  372  may be used to determine a current index or phase, which may be based the between-pixel fractional position of the current DDA position (e.g., corresponding to the spatial location of the output pixel). The index or phase may be used to select an appropriate set of coefficients from a set of filter coefficient tables  374 . Additionally, the filtering may be done per color component using same colored pixels. Thus, the filtering coefficients may be selected based not only on the phase of the current DDA position, but also the color of the current pixel. In one embodiment, 8 phases may be present between each input pixel and, thus, the vertical and horizontal scaling components may utilize 8-deep coefficient tables, such that the high-order 3 bits of the 16-bit fraction portion are used to express the current phase or index. Thus, as used herein, the term “raw image” data or the like shall be understood to refer to multi-color image data that is acquired by a single sensor with a color filter array pattern (e.g., Bayer) overlaying it, those providing multiple color components in one plane. In another embodiment, separate DDAs may be used for each color component. For instance, in such embodiments, the BCF  300  may extract the R, B, Gr, and Gb components from the raw image data and process each component as a separate plane. 
     In operation, horizontal and vertical scaling may include initializing the DDA  372  and performing the multi-tap polyphase filtering using the integer and fractional portions of the DDA  372 . While performed separately and with separate DDAs, the horizontal and vertical scaling operations are carried out in a similar manner. A step value or step size (DDAStepX for horizontal scaling and DDAStepY for vertical scaling) determines how much the DDA value (currDDA) is incremented after each output pixel is determined, and multi-tap polyphase filtering is repeated using the next currDDA value. For instance, if the step value is less than 1, then the image is up-scaled, and if the step value is greater than 1, the image is downscaled. If the step value is equal to 1, then no scaling occurs. Further, it should be noted that same or different step sizes may be used for horizontal and vertical scaling. 
     Output pixels are generated by the BCF  300  in the same order as input pixels (e.g., using the Bayer pattern). In the present embodiment, the input pixels may be classified as being even or odd based on their ordering. For instance, referring to  FIG. 33 , a graphical depiction of input pixel locations (row 375) and corresponding output pixel locations based on various DDAStep values (rows 376-380) are illustrated. In this example, the depicted row represents a row of red (R) and green (Gr) pixels in the raw Bayer image data. For horizontal filtering purposes, the red pixel at position 0.0 in the row 375 may be considered an even pixel, the green pixel at position 1.0 in the row 375 may be considered an odd pixel, and so forth. For the output pixel locations, even and odd pixels may be determined based on the least significant bit in the fraction portion (lower 16 bits) of the DDA  372 . For instance, assuming a DDAStep of 1.25, as shown in row 377, the least significant bit corresponds to the bit  14  of the DDA, as this bit gives a resolution of 0.25. Thus, the red output pixel at the DDA position (currDDA) 0.0 may be considered an even pixel (the least significant bit, bit  14 , is 0), the green output pixel at currDDA 1.0 (bit  14  is 1), and so forth. Further, while  FIG. 33  is discussed with respect to filtering in the horizontal direction (using DDAStepX), it should be understood that the determination of even and odd input and output pixels may be applied in the same manner with respect to vertical filtering (using DDAStepY). In other embodiments, the DDAs  372  may also be used to track locations of the input pixels (e.g., rather than track the desired output pixel locations). Further, it should be appreciated that DDAStepX and DDAStepY may be set to the same or different values. Further, assuming a Bayer pattern is used, it should be noted that the starting pixel used by the BCF  300  could be any one of a Gr, Gb, R, or B pixel depending, for instance, on which pixel is located at a corner within the active region  280 . 
     With this in mind, the even/odd input pixels are used to generate the even/odd output pixels, respectively. Given an output pixel location alternating between even and odd position, a center source input pixel location (referred to herein as “currPixel”) for filtering purposes is determined by the rounding the DDA to the closest even or odd input pixel location for even or odd output pixel locations (based on DDAStepX), respectively. In an embodiment where the DDA  372   a  is configured to use 16 bits to represent an integer and 16 bits to represent a fraction, currPixel may be determined for even and odd currDDA positions using Equations 6a and 6b below:
 
Even output pixel locations may be determined based on bits [31:16] of: (currDDA+1.0) &amp; 0 xFFFE. 0000  (6a)
 
Odd output pixel locations may be determined based on bits [31:16] of: (currDDA)|0 x 0001.0000  (6b)
 
Essentially, the above equations present a rounding operation, whereby the even and odd output pixel positions, as determined by currDDA, are rounded to the nearest even and odd input pixel positions, respectively, for the selection of currPixel.
 
     Additionally, a current index or phase (currindex) may also be determined at each currDDA position. As discussed above, the index or phase values represent the fractional between-pixel position of the output pixel position relative to the input pixel positions. For instance, in one embodiment, 8 phases may be defined between each input pixel position. For instance, referring again to  FIG. 33 , 8 index values  0 - 7  are provided between the first red input pixel at position 0.0 and the next red input pixel at position 2.0. Similarly, 8 index values  0 - 7  are provided between the first green input pixel at position 1.0 and the next green input pixel at position 3.0. In one embodiment, the currIndex values may be determined in accordance with Equations 7a and 7b below for even and odd output pixel locations, respectively:
 
Even output pixel locations may be determined based on bits [16:14] of: (currDDA+0.125)  (7a)
 
Odd output pixel locations may be determined based on bits [16:14] of: (currDDA+1.125)  (7b)
 
For the odd positions, the additional 1 pixel shift is equivalent to adding an offset of four to the coefficient index for odd output pixel locations to account for the index offset between different color components with respect to the DDA  372 .
 
     Once currPixel and currIndex have been determined at a particular currDDA location, the filtering process may select one or more neighboring same-colored pixels based on currPixel (the selected center input pixel). By way of example, in an embodiment where the horizontal scaling logic  368  includes a 5-tap polyphase filter and the vertical scaling logic  370  includes a 3-tap polyphase filter, two same-colored pixels on each side of currPixel in the horizontal direction may be selected for horizontal filtering (e.g., −2, −1, 0, +1, +2), and one same-colored pixel on each side of currPixel in the vertical direction may be selected for vertical filtering (e.g., −1, 0, +1). Further, currIndex may be used as a selection index to select the appropriate filtering coefficients from the filter coefficients table  374  to apply to the selected pixels. For instance, using the 5-tap horizontal/3-tap vertical filtering embodiment, five 8-deep tables may be provided for horizontal filtering, and three 8-deep tables may be provided for vertical filtering. Though illustrated as part of the BCF  300 , it should be appreciated that the filter coefficient tables  374  may, in certain embodiments, be stored in a memory that is physically separate from the BCF  300 , such as the memory  108 . 
     Before discussing the horizontal and vertical scaling operations in further detail, Table 4 below shows examples of how currPixel and currIndex values, as determined based on various DDA positions using different DDAStep values (e.g., could apply to DDAStepX or DDAStepY). 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Binning Compensation Filter - DDA Examples of currPixel and currIndex calculation 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Out- 
                   
                   
                   
                   
                   
                   
                   
                   
               
               
                 put 
               
               
                 Pixel 
               
               
                 (Even 
               
               
                 or 
                 DDAStep 
                 1.25 
                 DDAStep 
                 1.5 
                 DDAStep 
                 1.75 
                 DDAStep 
                 2.0 
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 Odd) 
                 currDDA 
                 currIndex 
                 currPixel 
                 currDDA 
                 currIndex 
                 currPixel 
                 currDDA 
                 currIndex 
                 currPixel 
                 currDDA 
                 currIndex 
                 currPixel 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 0 
                 0.0 
                 0 
                 0 
                 0.0 
                 0 
                 0 
                 0.0 
                 0 
                 0 
                 0.0 
                 0 
                 0 
               
               
                 1 
                 1.25 
                 1 
                 1 
                 1.5 
                 2 
                 1 
                 1.75 
                 3 
                 1 
                 2 
                 4 
                 3 
               
               
                 0 
                 2.5 
                 2 
                 2 
                 3 
                 4 
                 4 
                 3.5 
                 6 
                 4 
                 4 
                 0 
                 4 
               
               
                 1 
                 3.75 
                 3 
                 3 
                 4.5 
                 6 
                 5 
                 5.25 
                 1 
                 5 
                 6 
                 4 
                 7 
               
               
                 0 
                 5 
                 4 
                 6 
                 6 
                 0 
                 6 
                 7 
                 4 
                 8 
                 8 
                 0 
                 8 
               
               
                 1 
                 6.25 
                 5 
                 7 
                 7.5 
                 2 
                 7 
                 8.75 
                 7 
                 9 
                 10 
                 4 
                 11 
               
               
                 0 
                 7.5 
                 6 
                 8 
                 9 
                 4 
                 10 
                 10.5 
                 2 
                 10 
                 12 
                 0 
                 12 
               
               
                 1 
                 8.75 
                 7 
                 9 
                 10.5 
                 6 
                 11 
                 12.25 
                 5 
                 13 
                 14 
                 4 
                 15 
               
               
                 0 
                 10 
                 0 
                 10 
                 12 
                 0 
                 12 
                 14 
                 0 
                 14 
                 16 
                 0 
                 16 
               
               
                 1 
                 11.25 
                 1 
                 11 
                 13.5 
                 2 
                 13 
                 15.75 
                 3 
                 15 
                 18 
                 4 
                 19 
               
               
                 0 
                 12.5 
                 2 
                 12 
                 15 
                 4 
                 16 
                 17.5 
                 6 
                 18 
                 20 
                 0 
                 20 
               
               
                 1 
                 13.75 
                 3 
                 13 
                 16.5 
                 6 
                 17 
                 19.25 
                 1 
                 19 
                 22 
                 4 
                 23 
               
               
                 0 
                 15 
                 4 
                 16 
                 18 
                 0 
                 18 
                 21 
                 4 
                 22 
                 24 
                 0 
                 24 
               
               
                 1 
                 16.25 
                 5 
                 17 
                 19.5 
                 2 
                 19 
                 22.75 
                 7 
                 23 
                 26 
                 4 
                 27 
               
               
                 0 
                 17.5 
                 6 
                 18 
                 21 
                 4 
                 22 
                 24.5 
                 2 
                 24 
                 28 
                 0 
                 28 
               
               
                 1 
                 18.75 
                 7 
                 19 
                 22.5 
                 6 
                 23 
                 26.25 
                 5 
                 27 
                 30 
                 4 
                 31 
               
               
                 0 
                 20 
                 0 
                 20 
                 24 
                 0 
                 24 
                 28 
                 0 
                 28 
                 32 
                 0 
                 32 
               
               
                   
               
            
           
         
       
     
     To provide an example, let us assume that a DDA step size (DDAStep) of 1.5 is selected (row 378 of  FIG. 33 ), with the current DDA position (currDDA) beginning at 0, indicating an even output pixel position. To determine currPixel, Equation 6a may be applied, as shown below: 
               currDDA   =     0.0   ⁢     (   even   )         ⁢     
                       (   AND   )     ⁢           0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0001.   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (     currDDA   +   1.0     )                 1111   ⁢           ⁢   1111   ⁢           ⁢   1111   ⁢           ⁢   1110.0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (     0   ⁢   xFFFE   ⁢   .0000     )                       0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     _     ⁢   .0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     ⁢                     ⁢     
     ⁢           =     
     ⁢       currPixel   ⁢           ⁢     (     determined   ⁢           ⁢   as   ⁢           ⁢     bits   ⁡     [     31   ⁢     :     ⁢   16     ]       ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   result     )       =   0       ;         
Thus, at the currDDA position 0.0 (row 378), the source input center pixel for filtering corresponds to the red input pixel at position 0.0 of row 375.
 
     To determine currIndex at the even currDDA 0.0, Equation 7a may be applied, as shown below: 
               currDDA   =         0.0   ⁢     (   even   )       ⁢     
     ⁢     
     ⁢           +           ⁢           0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000.   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   currDDA   )                   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000.0010   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   0.125   )       ⁢                         0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   000   ⁢     0.00   _     ⁢   10   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     ⁢                     ⁢     
     ⁢           =     
     ⁢       currIndex   ⁢           ⁢     (     determined   ⁢           ⁢   as   ⁢           ⁢     bits   ⁡     [     16   ⁢     :     ⁢   14     ]       ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   result     )       =       [   000   ]     =   0           ;         
Thus, at the currDDA position 0.0 (row 378), a currIndex value of 0 may be used to select filtering coefficients from the filter coefficients table  374 .
 
     Accordingly, filtering (which may be vertical or horizontal depending on whether DDAStep is in the X (horizontal) or Y (vertical) direction) may applied based on the determined currPixel and currIndex values at currDDA 0.0, and the DDA  372  is incremented by DDAStep (1.5), and the next currPixel and currIndex values are determined. For instance, at the next currDDA position 1.5 (an odd position), currPixel may be determined using Equation 6b as follows: 
                     ⁢     currDDA   =     0.0   ⁢     (   odd   )         ⁢     
                       (   OR   )     ⁢           ⁢             0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0001.   ⁢           ⁢   1000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   currDDA   )       ⁢                       0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0001.0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (     0   ⁢   x   ⁢           ⁢   0001.0000     )                               ⁢     0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0001       _     ⁢   .0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     ⁢                     ⁢     
     ⁢           =     
     ⁢       currPixel   ⁢           ⁢     (     determined   ⁢           ⁢   as   ⁢           ⁢     bits   ⁡     [     31   ⁢     :     ⁢   16     ]       ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   result     )       =   1       ;         
Thus, at the currDDA position 1.5 (row 378), the source input center pixel for filtering corresponds to the green input pixel at position 1.0 of row 375.
 
     Further, currIndex at the odd currDDA 1.5 may be determined using Equation 7b, as shown below: 
               currDDA   =         1.5   ⁢     (   odd   )       ⁢     
     ⁢     
     ⁢           +           ⁢           0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0001.   ⁢           ⁢   1000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   currDDA   )                   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0001.0010   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   1.125   )       ⁢                         0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   001   ⁢       0.   ⁢           ⁢   10     _     ⁢   10   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     ⁢                     ⁢     
     ⁢           =     
     ⁢       currIndex   ⁢           ⁢     (     determined   ⁢           ⁢   as   ⁢           ⁢     bits   ⁡     [     16   ⁢     :     ⁢   14     ]       ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   result     )       =       [   010   ]     =   2           ;         
Thus, at the currDDA position 1.5 (row 378), a currIndex value of 2 may be used to select the appropriate filtering coefficients from the filter coefficients table  374 . Filtering (which may be vertical or horizontal depending on whether DDAStep is in the X (horizontal) or Y (vertical) direction) may thus be applied using these currPixel and currIndex values.
 
     Next, the DDA  372  is incremented again by DDAStep (1.5), resulting in a currDDA value of 3.0. The currPixel corresponding to currDDA 3.0 may be determined using Equation 6a, as shown below: 
                     ⁢     currDDA   =     3.0   ⁢     (   even   )         ⁢     
                       (   AND   )     ⁢           0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0100.   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (     currDDA   +   1.0     )                 1111   ⁢           ⁢   1111   ⁢           ⁢   1111   ⁢           ⁢   1110.0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (     0   ⁢   xFFFE   ⁢   .0000     )                       0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0100     _     ⁢   .0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     ⁢                     ⁢     
     ⁢           =     
     ⁢       currPixel   ⁢           ⁢     (     determined   ⁢           ⁢   as   ⁢           ⁢     bits   ⁡     [     31   ⁢     :     ⁢   16     ]       ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   result     )       =   4       ;         
Thus, at the currDDA position 3.0 (row 378), the source input center pixel for filtering corresponds to the red input pixel at position 4.0 of row 375.
 
     Next, currIndex at the even currDDA 3.0 may be determined using Equation 7a, as shown below: 
                     ⁢       currDDA   =         3.0   ⁢     (   even   )       ⁢     
     ⁢     
     ⁢           +           ⁢           0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0011.   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   currDDA   )                   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000.0010   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢     (   0.125   )       ⁢                         0000   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   001   ⁢       1.   ⁢           ⁢   00     _     ⁢   10   ⁢           ⁢   0000   ⁢           ⁢   0000   ⁢           ⁢   0000     ⁢                     ⁢     
     ⁢           =     
     ⁢       currIndex   ⁢           ⁢     (     determined   ⁢           ⁢   as   ⁢           ⁢     bits   ⁡     [     16   ⁢     :     ⁢   14     ]       ⁢           ⁢   of   ⁢           ⁢   the   ⁢           ⁢   result     )       =       [   100   ]     =   4           ;           
Thus, at the currDDA position 3.0 (row 378), a currIndex value of 4 may be used to select the appropriate filtering coefficients from the filter coefficients table  374 . As will be appreciated, the DDA  372  may continue to be incremented by DDAStep for each output pixel, and filtering (which may be vertical or horizontal depending on whether DDAStep is in the X (horizontal) or Y (vertical) direction) may be applied using the currPixel and currIndex determined for each currDDA value.
 
     As discussed above, currIndex may be used as a selection index to select the appropriate filtering coefficients from the filter coefficients table  374  to apply to the selected pixels. The filtering process may include obtaining the source pixel values around the center pixel (currPixel), multiplying each of the selected pixels by the appropriate filtering coefficients selected from the filter coefficients table  374  based on currIndex, and summing the results to obtain a value of the output pixel at the location corresponding to currDDA. Further, because the present embodiment utilizes 8 phases between same colored pixels, using the 5-tap horizontal/3-tap vertical filtering embodiment, five 8-deep tables may be provided for horizontal filtering, and three 8-deep tables may be provided for vertical filtering. In one embodiment, each of the coefficient table entries may include a 16-bit 2&#39;s complement fixed point number with 3 integer bits and 13 fraction bits. 
     Further, assuming a Bayer image pattern, in one embodiment, the vertical scaling component may include four separate 3-tap polyphase filters, one for each color component: Gr, R, B, and Gb. Each of the 3-tap filters may use the DDA  372  to control the stepping of the current center pixel and the index for the coefficients, as described above. Similarly, the horizontal scaling components may include four separate 5-tap polyphase filters, one for each color component: Gr, R, B, and Gb. Each of the 5-tap filters may use the DDA  372  to control the stepping (e.g., via DDAStep) of the current center pixel and the index for the coefficients. It should be understood however, that fewer or more taps could be utilized by the horizontal and vertical scalars in other embodiments. 
     For boundary cases, the pixels used in the horizontal and vertical filtering process may depend upon the relationship of the current DDA position (currDDA) relative to a frame border (e.g., border defined by the active region  280  in  FIG. 19 ). For instance, in horizontal filtering, if the currDDA position, when compared to the position of the center input pixel (SrcX) and the width (SrcWidth) of the frame (e.g., width  290  of the active region  280  of  FIG. 19 ) indicates that the DDA  372  is close to the border such that there are not enough pixels to perform the 5-tap filtering, then the same-colored input border pixels may be repeated. For instance, if the selected center input pixel is at the left edge of the frame, then the center pixel may be replicated twice for horizontal filtering. If the center input pixel is near the left edge of the frame such that only one pixel is available between the center input pixel and the left edge, then, for horizontal filtering purposes, the one available pixel is replicated in order to provide two pixel values to the left of the center input pixel. Further, the horizontal scaling logic  368  may be configured such that the number of input pixels (including original and replicated pixels) cannot exceed the input width. This may be expressed as follows:
 
Start X =(((DDAInit X+ 0 x 0001.0000) &amp; 0 xFFFE. 0000)&gt;&gt;16)
 
End X =(((DDAInit X +DDAStep X *(BCFOutWidth−1))|0 x 0001.0000)&gt;&gt;16)
 
End X −Start X &lt;=SrcWidth−1
 
wherein, DDAInitX represents the initial position of the DDA  372 , DDAStepX represents the DDA step value in the horizontal direction, and BCFOutWidth represents the width of the frame output by the BCF  300 .
 
     For vertical filtering, if the currDDA position, when compared to the position of the center input pixel (SrcY) and the width (SrcHeight) of the frame (e.g., width  290  of the active region  280  of  FIG. 19 ) indicates that the DDA  372  is close to the border such that there are not enough pixels to perform the 3-tap filtering, then the input border pixels may be repeated. Further, the vertical scaling logic  370  may be configured such that the number of input pixels (including original and replicated pixels) cannot exceed the input height. This may be expressed as follows:
 
Start Y =(((DDAInit Y+ 0 x 0001.0000) &amp; 0 xFFFE. 0000)&gt;&gt;16)
 
End Y =(((DDAInit Y +DDAStep Y *(BCFOutHeight−1))|0 x 0001.0000)&gt;&gt;16)
 
End Y −Start Y &lt;=SrcHeight−1
 
wherein, DDAInitY represents the initial position of the DDA  372 , DDAStepY represents the DDA step value in the vertical direction, and BCFOutHeight represents the width of the frame output by the BCF  300 .
 
     Referring now to  FIG. 34 , a flow chart depicting a method  382  for applying binning compensation filtering to image data received by the front-end pixel processing unit  130  in accordance with an embodiment. It will be appreciated that the method  382  illustrated in  FIG. 34  may apply to both vertical and horizontal scaling. Beginning at step  383  the DDA  372  is initialized and a DDA step value (which may correspond to DDAStepX for horizontal scaling and DDAStepY for vertical scaling) is determined. Next, at step  384 , a current DDA position (currDDA), based on DDAStep, is determined. As discussed above, currDDA may correspond to an output pixel location. Using currDDA, the method  382  may determine a center pixel (currPixel) from the input pixel data that may be used for binning compensation filtering to determine a corresponding output value at currDDA, as indicated at step  385 . Subsequently, at step  386 , an index corresponding to currDDA (currIndex) may be determined based on the fractional between-pixel position of currDDA relative to the input pixels (e.g., row 375 of  FIG. 33 ). By way of example, in an embodiment where the DDA includes 16 integer bits and 16 fraction bits, currPixel may be determined in accordance with Equations 6a and 6b, and currIndex may be determined in accordance with Equations 7a and 7b, as shown above. While the 16 bit integer/16 bit fraction configuration is described herein as one example, it should be appreciated that other configurations of the DDA  372  may be utilized in accordance with the present technique. By way of example, other embodiments of the DDA  372  may be configured to include a 12 bit integer portion and 20 bit fraction portion, a 14 bit integer portion and 18 bit fraction portion, and so forth. 
     Once currPixel and currIndex are determined, same-colored source pixels around currPixel may be selected for multi-tap filtering, as indicated by step  387 . For instance, as discussed above, one embodiment may utilize 5-tap polyphase filtering in the horizontal direction (e.g., selecting 2 same-colored pixels on each side of currPixel) and may utilize 3-tap polyphase filtering in the vertical direction (e.g., selecting 1 same-colored pixel on each side of currPixel). Next, at step  388 , once the source pixels are selected, filtering coefficients may be selected from the filter coefficients table  374  of the BCF  300  based upon currIndex. 
     Thereafter, at step  389 , filtering may be applied to the source pixels to determine the value of an output pixel corresponding to the position represented by currDDA. For instance, in one embodiment, the source pixels may be multiplied by their respective filtering coefficients, and the results may be summed to obtain the output pixel value. The direction in which filtering is applied at step  389  may be vertical or horizontal depending on whether DDAStep is in the X (horizontal) or Y (vertical) direction. Finally, at step  263 , the DDA  372  is incremented by DDAStep at step  390 , and the method  382  returns to step  384 , whereby the next output pixel value is determined using the binning compensation filtering techniques discussed herein. 
     Referring to  FIG. 35 , the step  385  for determining currPixel from the method  382  is illustrated in more detail in accordance with one embodiment. For instance, step  385  may include the sub-step  392  of determining whether the output pixel location corresponding to currDDA (from step  384 ) is even or odd. As discussed above, an even or odd output pixel may be determined based on the least significant bit of currDDA based on DDAStep. For instance, given a DDAStep of 1.25, a currDDA value of 1.25 may be determined as odd, since the least significant bit (corresponding to bit  14  of the fractional portion of the DDA  372 ) has a value of 1. For a currDDA value of 2.5, bit  14  is 0, thus indicating an even output pixel location. 
     At decision logic  393 , a determination is made as to whether the output pixel location corresponding to currDDA is even or odd. If the output pixel is even, decision logic  393  continues to sub-step  394 , wherein currPixel is determined by incrementing the currDDA value by 1 and rounding the result to the nearest even input pixel location, as represented by Equation 6a above. If the output pixel is odd, then decision logic  393  continues to sub-step  395 , wherein currPixel is determined by rounding the currDDA value to the nearest odd input pixel location, as represented by Equation 6b above. The currPixel value may then be applied to step  387  of the method  382  to select source pixels for filtering, as discussed above. 
     Referring also to  FIG. 36 , the step  386  for determining currIndex from the method  382  is illustrated in more detail in accordance with one embodiment. For instance, step  386  may include the sub-step  396  of determining whether the output pixel location corresponding to currDDA (from step  384 ) is even or odd. This determination may be performed in a similar manner as step  392  of  FIG. 35 . At decision logic  397 , a determination is made as to whether the output pixel location corresponding to currDDA is even or odd. If the output pixel is even, decision logic  397  continues to sub-step  398 , wherein currIndex is determined by incrementing the currDDA value by one index step determining currIndex based on the lowest order integer bit and the two highest order fraction bits of the DDA  372 . For instance, in an embodiment wherein 8 phases are provided between each same-colored pixel, and wherein the DDA includes 16 integer bits and 16 fraction bits, one index step may correspond to 0.125, and currIndex may be determined based on bits [16:14] of the currDDA value incremented by 0.125 (e.g., Equation 7a). If the output pixel is odd, decision logic  397  continues to sub-step  399 , wherein currIndex is determined by incrementing the currDDA value by one index step and one pixel shift, and determining currIndex based on the lowest order integer bit and the two highest order fraction bits of the DDA  372 . Thus, in an embodiment wherein 8 phases are provided between each same-colored pixel, and wherein the DDA includes 16 integer bits and 16 fraction bits, one index step may correspond to 0.125, one pixel shift may correspond to 1.0 (a shift of 8 index steps to the next same colored pixel), and currIndex may be determined based on bits [16:14] of the currDDA value incremented by 1.125 (e.g., Equation 7b). 
     While the presently illustrated embodiment provides the BCF  300  as a component of the front-end pixel processing unit  130 , other embodiments may incorporate the BCF  300  into a raw image data processing pipeline of the ISP pipe  82  which, as discussed further below, may include defective pixel detection/correction logic, gain/offset/compensation blocks, noise reduction logic, lens shading correction logic, and demosaicing logic. Further, in embodiments where the aforementioned defective pixel detection/correction logic, gain/offset/compensation blocks, noise reduction logic, lens shading correction logic do not rely upon the linear placement of the pixels, the BCF  300  may be incorporated with the demosaicing logic to perform binning compensation filtering and reposition the pixels prior to demoasicing, as demosaicing generally does rely upon the even spatial positioning of the pixels. For instance, in one embodiment, the BCF  300  may be incorporated anywhere between the sensor input and the demosaicing logic, with temporal filtering and/or defective pixel detection/correction being applied to the raw image data prior to binning compensation. 
     As discussed above the output of the BCF  300 , which may be the output FEProcOut ( 109 ) having spatially evenly distributed image data (e.g., sample  360  of  FIG. 31 ), may be forwarded to the ISP pipe processing logic  82  for additional processing. However, before shifting the focus of this discussion to the ISP pipe processing logic  82 , a more detailed description of various functionalities that may be provided by the statistics processing units (e.g.,  122  and  124 ) that may be implemented in the ISP front-end logic  80  will first be provided. 
     Referring back to the general description of the statistics processing units  120  and  122 , these units may be configured to collect various statistics about the image sensors that capture and provide the raw image signals (Sif 0  and Sif 1 ), such as statistics relating to auto-exposure, auto-white balance, auto-focus, flicker detection, black level compensation, and lens shading correction, and so forth. In doing so, the statistics processing units  120  and  122  may first apply one or more image processing operations to their respective input signals, Sif 0  (from Sensor 0 ) and Sif 1  (from Sensor 1 ). 
     For example, referring to  FIG. 37 , a more detailed block diagram view of the statistics processing unit  120  associated with Sensor 0  ( 90   a ) is illustrated in accordance with one embodiment. As shown, the statistics processing unit  120  may include the following functional blocks: defective pixel detection and correction logic  460 , black level compensation (BLC) logic  462 , lens shading correction logic  464 , inverse BLC logic  466 , and statistics collection logic  468 . Each of these functional blocks will be discussed below. Further, it should be understood that the statistics processing unit  122  associated with Sensor 1  ( 90   b ) may be implemented in a similar manner. 
     Initially, the output of selection logic  124  (e.g., Sif 0  or SifIn 0 ) is received by the front-end defective pixel correction logic  460 . As will be appreciated, “defective pixels” may be understood to refer to imaging pixels within the image sensor(s)  90  that fail to sense light levels accurately. Defective pixels may attributable to a number of factors, and may include “hot” (or leaky) pixels, “stuck” pixels, and “dead pixels.” A “hot” pixel generally appears as being brighter than a non-defective pixel given the same amount of light at the same spatial location. Hot pixels may result due to reset failures and/or high leakage. For example, a hot pixel may exhibit a higher than normal charge leakage relative to non-defective pixels, and thus may appear brighter than non-defective pixels. Additionally, “dead” and “stuck” pixels may be the result of impurities, such as dust or other trace materials, contaminating the image sensor during the fabrication and/or assembly process, which may cause certain defective pixels to be darker or brighter than a non-defective pixel, or may cause a defective pixel to be fixed at a particular value regardless of the amount of light to which it is actually exposed. Additionally, dead and stuck pixels may also result from circuit failures that occur during operation of the image sensor. By way of example, a stuck pixel may appear as always being on (e.g., fully charged) and thus appears brighter, whereas a dead pixel appears as always being off. 
     The defective pixel detection and correction (DPDC) logic  460  in the ISP front-end logic  80  may correct (e.g., replace defective pixel values) defective pixels before they are considered in statistics collection (e.g.,  468 ). In one embodiment, defective pixel correction is performed independently for each color component (e.g., R, B, Gr, and Gb for a Bayer pattern). Generally, the front-end DPDC logic  460  may provide for dynamic defect correction, wherein the locations of defective pixels are determined automatically based upon directional gradients computed using neighboring pixels of the same color. As will be understand, the defects may be “dynamic” in the sense that the characterization of a pixel as being defective at a given time may depend on the image data in the neighboring pixels. By way of example, a stuck pixel that is always on maximum brightness may not be regarded as a defective pixel if the location of the stuck pixel is in an area of the current image that is dominate by brighter or white colors. Conversely, if the stuck pixel is in a region of the current image that is dominated by black or darker colors, then the stuck pixel may be identified as a defective pixel during processing by the DPDC logic  460  and corrected accordingly. 
     The DPDC logic  460  may utilize one or more horizontal neighboring pixels of the same color on each side of a current pixel to determine if the current pixel is defective using pixel-to-pixel directional gradients. If a current pixel is identified as being defective, the value of the defective pixel may be replaced with the value of a horizontal neighboring pixel. For instance, in one embodiment, five horizontal neighboring pixels of the same color that are inside the raw frame  278  ( FIG. 19 ) boundary are used, wherein the five horizontal neighboring pixels include the current pixel and two neighboring pixels on either side. Thus, as illustrated in  FIG. 38 , for a given color component c and for the current pixel P, horizontal neighbor pixels P 0 , P 1 , P 2 , and P 3  may be considered by the DPDC logic  460 . It should be noted, however, that depending on the location of the current pixel P, pixels outside the raw frame  278  are not considered when calculating pixel-to-pixel gradients. 
     For instance, as shown in  FIG. 38 , in a “left edge” case  470 , the current pixel P is at the leftmost edge of the raw frame  278  and, thus, the neighboring pixels P 0  and P 1  outside of the raw frame  278  are not considered, leaving only the pixels P, P 2 , and P 3  (N=3). In a “left edge+1” case  472 , the current pixel P is one unit pixel away from the leftmost edge of the raw frame  278  and, thus, the pixel P 0  is not considered. This leaves only the pixels P 1 , P, P 2 , and P 3  (N=4). Further, in a “centered” case  474 , pixels P 0  and P 1  on the left side of the current pixel P and pixels P 2  and P 3  on the right side of the current pixel P are within the raw frame  278  boundary and, therefore, all of the neighboring pixels P 0 , P 1 , P 2 , and P 3  (N=5) are considered in calculating pixel-to-pixel gradients. Additionally, similar cases  476  and  478  may be encountered as the rightmost edge of the raw frame  278  is approached. For instance, given the “right edge−1” case  476 , the current pixel P is one unit pixel away the rightmost edge of the raw frame  278  and, thus, the pixel P 3  is not considered (N=4). Similarly, in the “right edge” case  478 , the current pixel P is at the rightmost edge of the raw frame  278  and, thus, both of the neighboring pixels P 2  and P 3  are not considered (N=3). 
     In the illustrated embodiment, for each neighboring pixel (k=0 to 3) within the picture boundary (e.g., raw frame  278 ), the pixel-to-pixel gradients may be calculated as follows:
 
 G   k =abs( P−P   k ), for 0≦ k≦ 3 (only for  k  within the raw frame)  (8)
 
Once the pixel-to-pixel gradients have been determined, defective pixel detection may be performed by the DPDC logic  460  as follows. First, it is assumed that a pixel is defective if a certain number of its gradients G k  are at or below a particular threshold, denoted by the variable dprTh. Thus, for each pixel, a count (C) of the number of gradients for neighboring pixels inside the picture boundaries that are at or below the threshold dprTh is accumulated. By way of example, for each neighbor pixel inside the raw frame  278 , the accumulated count C of the gradients G k  that are at or below the threshold dprTh may be computed as follows:
 
                     C   =       ∑   k   N     ⁢     (       G   k     ≤   dprTh     )         ,     
     ⁢       for   ⁢           ⁢   0     ≤   k   ≤     3   ⁢     (     only   ⁢           ⁢   for   ⁢           ⁢   k   ⁢           ⁢   within   ⁢           ⁢   the   ⁢           ⁢   raw   ⁢           ⁢   frame     )                 (   9   )               
As will be appreciated, depending on the color components, the threshold value dprTh may vary. Next, if the accumulated count C is determined to be less than or equal to a maximum count, denoted by the variable dprMaxC, then the pixel may be considered defective. This logic is expressed below:
 
if ( C≦dpr Max C ), then the pixel is defective.  (10)
 
     Defective pixels are replaced using a number of replacement conventions. For instance, in one embodiment, a defective pixel may be replaced with the pixel to its immediate left, P 1 . At a boundary condition (e.g., P 1  is outside of the raw frame  278 ), a defective pixel may replaced with the pixel to its immediate right, P 2 . Further, it should be understood that replacement values may be retained or propagated for successive defective pixel detection operations. For instance, referring to the set of horizontal pixels shown in  FIG. 38 , if P 0  or P 1  were previously identified by the DPDC logic  460  as being defective pixels, their corresponding replacement values may be used for the defective pixel detection and replacement of the current pixel P. 
     To summarize the above-discussed defective pixel detection and correction techniques, a flow chart depicting such a process is provided in  FIG. 39  and referred to by reference number  480 . As shown, process  480  begins at step  482 , at which a current pixel (P) is received and a set of neighbor pixels is identified. In accordance with the embodiment described above, the neighbor pixels may include two horizontal pixels of the same color component from opposite sides of the current pixel (e.g., P 0 , P 1 , P 2 , and P 3 ). Next, at step  484 , horizontal pixel-to-pixel gradients are calculated with respect to each neighboring pixel within the raw frame  278 , as described in Equation 8 above. Thereafter, at step  486 , a count C of the number of gradients that are less than or equal to a particular threshold dprTh is determined. As shown at decision logic  488 , if C is less than or equal to dprMaxC, then the process  480  continues to step  490 , and the current pixel is identified as being defective. The defective pixel is then corrected at step  492  using a replacement value. Additionally, referring back to decision logic  488 , if C is greater than dprMaxC, then the process continues to step  494 , and the current pixel is identified as not being defective, and its value is not changed. 
     It should be noted that the defective pixel detection/correction techniques applied during the ISP front-end statistics processing may be less robust than defective pixel detection/correction that is performed in the ISP pipe logic  82 . For instance, as will be discussed in further detail below, defective pixel detection/correction performed in the ISP pipe logic  82  may, in addition to dynamic defect correction, further provide for fixed defect correction, wherein the locations of defective pixels are known a priori and loaded in one or more defect tables. Further, dynamic defect correction may in the ISP pipe logic  82  may also consider pixel gradients in both horizontal and vertical directions, and may also provide for the detection/correction of speckling, as will be discussed below. 
     Returning to  FIG. 37 , the output of the DPDC logic  460  is then passed to the black level compensation (BLC) logic  462 . The BLC logic  462  may provide for digital gain, offset, and clipping independently for each color component “c” (e.g., R, B, Gr, and Gb for Bayer) on the pixels used for statistics collection. For instance, as expressed by the following operation, the input value for the current pixel is first offset by a signed value, and then multiplied by a gain.
 
 Y =( X+O[c ])× G[c],   (11)
 
wherein X represents the input pixel value for a given color component c (e.g., R, B, Gr, or Gb), O[c] represents a signed 16-bit offset for the current color component c, and G[c] represents a gain value for the color component c. In one embodiment, the gain G[c] may be a 16-bit unsigned number with 2 integer bits and 14 fraction bits (e.g., 2.14 in floating point representation), and the gain G[c] may be applied with rounding. By way of example only, the gain G[c] may have a range of between 0 to 4X (e.g., 4 times the input pixel value).
 
     Next, as shown by Equation 12 below, the computed value Y, which is signed, may then be then clipped to a minimum and maximum range:
 
 Y =( Y &lt;min[ c ])?min[ c ]:( Y &gt;max[ c ])?max[ c]:Y )  (12)
 
     The variables min[c] and max[c] may represent signed 16-bit “clipping values for the minimum and maximum output values, respectively. In one embodiment, the BLC logic  462  may also be configured to maintain a count of the number of pixels that were clipped above and below maximum and minimum, respectively, per color component. 
     Subsequently, the output of the BLC logic  462  is forwarded to the lens shading correction (LSC) logic  464 . The LSC logic  464  may be configured to apply an appropriate gain on a per-pixel basis to compensate for drop-offs in intensity, which are generally roughly proportional to the distance from the optical center of the lens  88  of the imaging device  30 . As can be appreciated, such drop-offs may be the result of the geometric optics of the lens. By way of example, a lens having ideal optical properties may be modeled as the fourth power of the cosine of the incident angle, cos 4 (θ), referred to as the cos 4  law. However, because lens manufacturing is not perfect, various irregularities in the lens may cause the optical properties to deviate from the assumed cos 4  model. For instance, the thinner edged of the lens usually exhibits the most irregularities. Additionally, irregularities in lens shading patterns may also be the result of a microlens array within an image sensor not being perfectly aligned with the color array filter. Further, the infrared (IR) filter in some lenses may cause the drop-off to be illuminant-dependent and, thus, lens shading gains may be adapted depending upon the light source detected. 
     Referring to  FIG. 40 , a three-dimensional profile  496  depicting light intensity versus pixel position for a typical lens is illustrated. As shown, the light intensity near the center  498  of the lens gradually drops off towards the corners or edges  500  of the lens. The lens shading irregularities depicted in  FIG. 40  may be better illustrated by  FIG. 41 , which shows a colored drawing of an image  502  that exhibits drop-offs in light intensity towards the corners and edges. Particularly, it should be noted that the light intensity at the approximate center of the image appears to be brighter than the light intensity at the corners and/or edges of the image. 
     In accordance with embodiments of the present techniques, lens shading correction gains may be specified as a two-dimensional grid of gains per color channel (e.g., Gr, R, B, Gb for a Bayer filter). The gain grid points may be distributed at fixed horizontal and vertical intervals within the raw frame  278  ( FIG. 19 ). As discussed above in  FIG. 19 , the raw frame  278  may include an active region  280  which defines an area on which processing is performed for a particular image processing operation. With regard to the lens shading correction operation, an active processing region, which may be referred to as the LSC region, is defined within the raw frame region  278 . As will be discussed below, the LSC region must be completely inside or at the gain grid boundaries, otherwise results may be undefined. 
     For instance, referring to  FIG. 42 , an LSC region  504  and a gain grid  506  that may be defined within the raw frame  278  are shown. The LSC region  504  may have a width  508  and a height  510 , and may be defined by an x-offset  512  and a y-offset  514  with respect to the boundary of the raw frame  278 . Grid offsets (e.g., grid x-offset  516  and grid y-offset  518 ) from the base  520  of the grid gains  506  to the first pixel  522  in the LSC region  504  is also provided. These offsets may be within the first grid interval for a given color component. The horizontal α-direction) and vertical (y-direction) grid point intervals  524  and  526 , respectively, may be specified independently for each color channel. 
     As discussed above, assuming the use of a Bayer color filter array, 4 color channels of grid gains (R, B, Gr, and Gb) may be defined. In one embodiment, a total of 4K (4096) grid points may be available, and for each color channel, a base address for the start location of grid gains may be provided, such as by using a pointer. Further, the horizontal ( 524 ) and vertical ( 526 ) grid point intervals may be defined in terms of pixels at the resolution of one color plane and, in certain embodiments, may be provide for grid point intervals separated by a power of 2, such as by 8, 16, 32, 64, or 128, etc., in horizontal and vertical directions. As can be appreciated, by utilizing a power of 2, efficient implementation of gain interpolation using a shift (e.g., division) and add operations may be achieved. Using these parameters, the same gain values can be used even as the image sensor cropping region is changing. For instance, only a few parameters need to be updated to align the grid points to the cropped region (e.g., updating the grid offsets  524  and  526 ) instead of updating all grid gain values. By way of example only, this may be useful when cropping is used during digital zooming operations. Further, while the gain grid  506  shown in the embodiment of  FIG. 42  is depicted as having generally equally spaced grid points, it should be understood that in other embodiments, the grid points may not necessarily be equally spaced. For instance, in some embodiments, the grid points may be distributed unevenly (e.g., logarithmically), such that the grid points are less concentrated in the center of the LSC region  504 , but more concentrated towards the corners of the LSC region  504 , typically where lens shading distortion is more noticeable. 
     In accordance with the presently disclosed lens shading correction techniques, when a current pixel location is located outside of the LSC region  504 , no gain is applied (e.g., the pixel is passed unchanged). When the current pixel location is at a gain grid location, the gain value at that particular grid point may be used. However, when a current pixel location is between grid points, the gain may be interpolated using bi-linear interpolation. An example of interpolating the gain for the pixel location “G” on  FIG. 43  is provided below. 
     As shown in  FIG. 43 , the pixel G is between the grid points G 0 , G 1 , G 2 , and G 3 , which may correspond to the top-left, top-right, bottom-left, and bottom-right gains, respectively, relative to the current pixel location G. The horizontal and vertical size of the grid interval is represented by X and Y, respectively. Additionally, ii and jj represent the horizontal and vertical pixel offsets, respectively, relative to the position of the top left gain G 0 . Based upon these factors, the gain corresponding to the position G may thus be interpolated as follows: 
                   G   =               (     G   ⁢           ⁢   0   ⁢     (     Y   -   jj     )     ⁢     (     X   -   ii     )       )     +     (     G   ⁢           ⁢   1   ⁢     (     Y   -   jj     )     ⁢     (   ii   )       )     +                 (     G   ⁢           ⁢   2   ⁢     (   jj   )     ⁢     (     X   -   ii     )       )     +     (     G   ⁢           ⁢   3   ⁢     (   ii   )     ⁢     (   jj   )       )             XY             (     13   ⁢   a     )               
The terms in Equation 13a above may then be combined to obtain the following expression:
 
                   G   =               G   ⁢           ⁢     0   ⁡     [     XY   -     X   ⁡     (   jj   )       -     Y   ⁡     (   ii   )       +       (   ii   )     ⁢     (   jj   )         ]         +                 G   ⁢           ⁢     1   ⁡     [       Y   ⁡     (   ii   )       -       (   ii   )     ⁢     (   jj   )         ]         +     G   ⁢           ⁢     2   ⁡     [       X   ⁡     (   jj   )       -       (   ii   )     ⁢     (   jj   )         ]         +     G   ⁢           ⁢     3   ⁡     [       (   ii   )     ⁢     (   jj   )       ]                 XY             (     13   ⁢   b     )               
In one embodiment, the interpolation method may be performed incrementally, instead of using a multiplier at each pixel, thus reducing computational complexity. For instance, the term (ii)(jj) may be realized using an adder that may be initialized to 0 at location (0, 0) of the gain grid  506  and incremented by the current row number each time the current column number increases by a pixel. As discussed above, since the values of X and Y may be selected as powers of two, gain interpolation may be accomplished using a simple shift operations. Thus, the multiplier is needed only at the grid point G 0  (instead of at every pixel), and only addition operations are needed to determine the interpolated gain for the remaining pixels.
 
     In certain embodiments, the interpolation of gains between the grid points may use 14-bit precision, and the grid gains may be unsigned 10-bit values with 2 integer bits and 8 fractional bits (e.g., 2.8 floating point representation). Using this convention, the gain may have a range of between 0 and 4×, and the gain resolution between grid points may be 1/256. 
     The lens shading correction techniques may be further illustrated by the process  528  shown in  FIG. 44 . As shown, process  528  begins at step  530 , at which the position of a current pixel is determined relative to the boundaries of the LSC region  504  of  FIG. 42 . Next, decision logic  532  determines whether the current pixel position is within the LSC region  504 . If the current pixel position is outside of the LSC region  504 , the process  528  continues to step  534 , and no gain is applied to the current pixel (e.g., the pixel passes unchanged). 
     If the current pixel position is within the LSC region  504 , the process  528  continues to decision logic  536 , at which it is further determined whether the current pixel position corresponds to a grid point within the gain grid  504 . If the current pixel position corresponds to a grid point, then the gain value at that grid point is selected and applied to the current pixel, as shown at step  538 . If the current pixel position does not correspond to a grid point, then the process  528  continues to step  540 , and a gain is interpolated based upon the bordering grid points (e.g., G 0 , G 1 , G 2 , and G 3  of  FIG. 43 ). For instance, the interpolated gain may be computed in accordance with Equations 13a and 13b, as discussed above. Thereafter, the process  528  ends at step  542 , at which the interpolated gain from step  540  is applied to the current pixel. 
     As will be appreciated, the process  528  may be repeated for each pixel of the image data. For instance, as shown in  FIG. 45 , a three-dimensional profile depicting the gains that may be applied to each pixel position within a LSC region (e.g.  504 ) is illustrated. As shown, the gain applied at the corners  544  of the image may be generally greater than the gain applied to the center  546  of the image due to the greater drop-off in light intensity at the corners, as shown in  FIGS. 40 and 41 . Using the presently described lens shading correction techniques, the appearance of light intensity drop-offs in the image may be reduced or substantially eliminated. For instance,  FIG. 46  provides an example of how the colored drawing of the image  502  from  FIG. 41  may appear after lens shading correction is applied. As shown, compared to the original image from  FIG. 41 , the overall light intensity is generally more uniform across the image. Particularly, the light intensity at the approximate center of the image may be substantially equal to the light intensity values at the corners and/or edges of the image. Additionally, as mentioned above, the interpolated gain calculation (Equations 13a and 13b) may, in some embodiments, be replaced with an additive “delta” between grid points by taking advantage of the sequential column and row incrementing structure. As will be appreciated, this reduces computational complexity. 
     In further embodiments, in addition to using grid gains, a global gain per color component that is scaled as a function of the distance from the image center is used. The center of the image may be provided as an input parameter, and may be estimated by analyzing the light intensity amplitude of each image pixel in the uniformly illuminated image. The radial distance between the identified center pixel and the current pixel, may then be used to obtain a linearly scaled radial gain, G r , as shown below:
 
 G   r   =G   p   [c]×R,   (14)
 
wherein G p [c] represents a global gain parameter for each color component c (e.g., R, B, Gr, and Gb components for a Bayer pattern), and wherein R represents the radial distance between the center pixel and the current pixel.
 
     With reference to  FIG. 47 , which shows the LSC region  504  discussed above, the distance R may be calculated or estimated using several techniques. As shown, the pixel C corresponding to the image center may have the coordinates (x 0 , y 0 ), and the current pixel G may have the coordinates (x G , y G ). In one embodiment, the LSC logic  464  may calculate the distance R using the following equation:
 
 R =√{square root over (( x   G   −x   0 ) 2 +( y   G   −y   0 ) 2 )}{square root over (( x   G   −x   0 ) 2 +( y   G   −y   0 ) 2 )}  (15)
 
     In another embodiment, a simpler estimation formula, shown below, may be utilized to obtain an estimated value for R.
 
 R =α×max(abs( x   G   −x   0 ),abs( y   G   −y   0 ))+β×min(abs( x   G   −x   0 ),abs( y   G   −y   0 ))  (16)
 
In Equation 16, the estimation coefficients α and β may be scaled to 8-bit values. By way of example only, in one embodiment, a may be equal to approximately 123/128 and β may be equal to approximately 51/128 to provide an estimated value for R. Using these coefficient values, the largest error may be approximately 4%, with a median error of approximately 1.3%. Thus, even though the estimation technique may be somewhat less accurate than utilizing the calculation technique in determining R (Equation 15), the margin of error is low enough that the estimated values or R are suitable for determining radial gain components for the present lens shading correction techniques.
 
     The radial gain G r  may then be multiplied by the interpolated grid gain value G (Equations 13a and 13b) for the current pixel to determine a total gain that may be applied to the current pixel. The output pixel Y is obtained by multiplying the input pixel value X with the total gain, as shown below:
 
 Y =( G×G   r   ×X )  (17)
 
Thus, in accordance with the present technique, lens shading correction may be performed using only the interpolated gain, both the interpolated gain and the radial gain components. Alternatively, lens shading correction may also be accomplished using only the radial gain in conjunction with a radial grid table that compensates for radial approximation errors. For example, instead of a rectangular gain grid  506 , as shown in  FIG. 42 , a radial gain grid having a plurality of grid points defining gains in the radial and angular directions may be provided. Thus, when determining the gain to apply to a pixel that does not align with one of the radial grid points within the LSC region  504 , interpolation may be applied using the four grid points that enclose the pixel to determine an appropriate interpolated lens shading gain.
 
     Referring to  FIG. 48 , the use of interpolated and radial gain components in lens shading correction is illustrated by the process  548 . It should be noted that the process  548  may include steps that are similar to the process  528 , described above in  FIG. 44 . Accordingly, such steps have been numbered with like reference numerals. Beginning at step  530 , the current pixel is received and its location relative to the LSC region  504  is determined. Next, decision logic  532  determines whether the current pixel position is within the LSC region  504 . If the current pixel position is outside of the LSC region  504 , the process  548  continues to step  534 , and no gain is applied to the current pixel (e.g., the pixel passes unchanged). If the current pixel position is within the LSC region  504 , then the process  548  may continue simultaneously to step  550  and decision logic  536 . Referring first to step  550 , data identifying the center of the image is retrieved. As discussed above, determining the center of the image may include analyzing light intensity amplitudes for the pixels under uniform illumination. This may occur during calibration, for instance. Thus, it should be understood that step  550  does not necessarily encompass repeatedly calculating the center of the image for processing each pixel, but may refer to retrieving the data (e.g., coordinates) of previously determined image center. Once the center of the image is identified, the process  548  may continue to step  552 , wherein the distance between the image center and the current pixel location (R) is determined. As discussed above, the value of R may be calculated (Equation 15) or estimated (Equation 16). Then, at step  554 , a radial gain component G r  may be computed using the distance R and global gain parameter corresponding to the color component of the current pixel (Equation 14). The radial gain component G r  may be used to determine the total gain, as will be discussed in step  558  below. 
     Referring back to decision logic  536 , a determined whether the current pixel position corresponds to a grid point within the gain grid  504 . If the current pixel position corresponds to a grid point, then the gain value at that grid point is determined, as shown at step  556 . If the current pixel position does not correspond to a grid point, then the process  548  continues to step  540 , and an interpolated gain is computed based upon the bordering grid points (e.g., G 0 , G 1 , G 2 , and G 3  of  FIG. 43 ). For instance, the interpolated gain may be computed in accordance with Equations 13a and 13b, as discussed above. Next, at step  558 , a total gain is determined based upon the radial gain determined at step  554 , as well as one of the grid gains (step  556 ) or the interpolated gain ( 540 ). As can be appreciated, this may depend on which branch decision logic  536  takes during the process  548 . The total gain is then applied to the current pixel, as shown at step  560 . Again, it should be noted that like the process  528 , the process  548  may also be repeated for each pixel of the image data. 
     The use of the radial gain in conjunction with the grid gains may offer various advantages. For instance, using a radial gain allows for the use of single common gain grid for all color components. This may greatly reduce the total storage space required for storing separate gain grids for each color component. For instance, in a Bayer image sensor, the use of a single gain grid for each of the R, B, Gr, and Gb components may reduce the gain grid data by approximately 75%. As will be appreciated, this reduction in grid gain data may decrease implementation costs, as grid gain data tables may account for a significant portion of memory or chip area in image processing hardware. Further, depending upon the hardware implementation, the use of a single set of gain grid values may offer further advantages, such as reducing overall chip area (e.g., such as when the gain grid values are stored in an on-chip memory) and reducing memory bandwidth requirements (e.g., such as when the gain grid values are stored in an off-chip external memory). 
     Having thoroughly described the functionalities of the lens shading correction logic  464  shown in  FIG. 37 , the output of the LSC logic  464  is subsequently forwarded to the inverse black level compensation (IBLC) logic  466 . The IBLC logic  466  provides gain, offset and clip independently for each color component (e.g., R, B, Gr, and Gb), and generally performs the inverse function to the BLC logic  462 . For instance, as shown by the following operation, the value of the input pixel is first multiplied by a gain and then offset by a signed value.
 
 Y =( X×G[c ])+ O[c],   (18)
 
wherein X represents the input pixel value for a given color component c (e.g., R, B, Gr, or Gb), O[c] represents a signed 16-bit offset for the current color component c, and G[c] represents a gain value for the color component c. In one embodiment, the gain G[c] may have a range of between approximately 0 to 4X (4 times the input pixel value X). It should be noted that these variables may be the same variables discussed above in Equation 11. The computed value Y may be clipped to a minimum and maximum range using, for example, Equation 12. In one embodiment, the IBLC logic  466  may be configured to maintain a count of the number of pixels that were clipped above and below maximum and minimum, respectively, per color component.
 
     Thereafter, the output of the IBLC logic  466  is received by the statistics collection block  468 , which may provide for the collection of various statistical data points about the image sensor(s)  90 , such as those relating to auto-exposure (AE), auto-white balance (AWB), auto-focus (AF), flicker detection, and so forth. With this in mind, a description certain embodiments of the statistics collection block  468  and various aspects related thereto is provided below with respect to  FIGS. 48-66 . 
     As will be appreciated, AWB, AE, and AF statistics may be used in the acquisition of images in digital still cameras as well as video cameras. For simplicity, AWB, AE, and AF statistics may be collectively referred to herein as “3A statistics.” In the embodiment of the ISP front-end logic illustrated in  FIG. 37 , the architecture for the statistics collection logic  468  (“3A statistics logic”) may be implemented in hardware, software, or a combination thereof. Further, control software or firmware may be utilized to analyze the statistics data collected by the 3A statistics logic  468  and control various parameters of the lens (e.g., focal length), sensor (e.g., analog gains, integration times), and the ISP pipeline  82  (e.g., digital gains, color correction matrix coefficients). In certain embodiments, the image processing circuitry  32  may be configured to provide flexibility in statistics collection to enable control software or firmware to implement various AWB, AE, and AF algorithms. 
     With regard to white balancing (AWB), the image sensor response at each pixel may depend on the illumination source, since the light source is reflected from objects in the image scene. Thus, each pixel value recorded in the image scene is related to the color temperature of the light source. For instance,  FIG. 48  shows a graph  570  illustrating the color range of white areas under low color and high color temperatures for a YCbCr color space. As shown, the x-axis of the graph  570  represents the blue-difference chroma (Cb) and the y-axis of the graph  570  represents red-difference chroma (Cr) of the YCbCr color space. The graph  570  also shows a low color temperature axis  572  and a high color temperature axis  574 . The region  576  in which the axes  572  and  574  are positioned, represents the color range of white areas under low and high color temperatures in the YCbCr color space. It should be understood, however, that the YCbCr color space is merely one example of a color space that may be used in conjunction with auto white balance processing in the present embodiment. Other embodiments may utilize any suitable color space. For instance, in certain embodiments, other suitable color spaces may include a Lab (CIELab) color space (e.g., based on CIE 1976), a red/blue normalized color space (e.g., an R/(R+2G+B) and B/(R+2G+B) color space; a R/G and B/G color space; a Cb/Y and Cr/Y color space, etc.). Accordingly, for the purposes of this disclosure, the axes of the color space used by the 3A statistics logic  468  may be referred to as C1 and C2 (as is the case in  FIG. 49 ). 
     When a white object is illuminated under a low color temperature, it may appear reddish in the captured image. Conversely, a white object that is illuminated under a high color temperature may appear bluish in the captured image. The goal of white balancing is, therefore, to adjust RGB values such that the image appears to the human eye as if it were taken under canonical light. Thus, in the context of imaging statistics relating to white balance, color information about white objects are collected to determine the color temperature of the light source. In general, white balance algorithms may include two main steps. First, the color temperature of the light source is estimated. Second, the estimated color temperature is used to adjust color gain values and/or determine/adjust coefficients of a color correction matrix. Such gains may be a combination of analog and digital image sensor gains, as well as ISP digital gains. 
     For instance, in some embodiments, the imaging device  30  may be calibrated using multiple different reference illuminants. Accordingly, the white point of the current scene may be determined by selecting the color correction coefficients corresponding to a reference illuminant that most closely matches the illuminant of the current scene. By way of example only, one embodiment may calibrate the imaging device  30  using five reference illuminants, a low color temperature illuminant, a middle-low color temperature illuminant, a middle color temperature illuminant, a middle-high color temperature illuminant, and a high color temperature illuminant. As shown in  FIG. 50 , one embodiment may define white balance gains using the following color correction profiles: Horizon (H) (simulating a color temperature of approximately 2300 degrees), Incandescent (A or IncA) (simulating a color temperature of approximately 2856 degrees), D50 (simulating a color temperature of approximately 5000 degrees), D65 (simulating a color temperature of approximately 6500 degrees), and D75 (simulating a color temperature of approximately 7500 degrees). 
     Depending on the illuminant of the current scene, white balance gains may be determined using the gains corresponding to the reference illuminant that most closely matches the current illuminant. For instance, if the statistics logic  468  (described in more detail in  FIG. 51  below) determines that the current illuminant approximately matches the reference middle color temperature illuminant, D50, then white balance gains of approximately 1.37 and 1.23 may be applied to the red and blue color channels, respectively, while approximately no gain (1.0) is applied to the green channels (G 0  and G 1  for Bayer data). In some embodiments, if the current illuminant color temperature is in between two reference illuminants, white balance gains may be determined via interpolating the white balance gains between the two reference illuminants. Further, while the present example shows an imaging device being calibrated using H, A, D50, D65, and D75 illuminants, it should be understood that any suitable type of illuminant may be used for camera calibration, such as TL84 or CWF (fluorescent reference illuminants), and so forth. 
     As will be discussed further below, several statistics may be provided for AWB including a two-dimensional (2D) color histogram, and RGB or YCC sums to provide multiple programmable color ranges. For instance, in one embodiment, the statistics logic  468  may provide a set of multiple pixel filters, of which a subset of the multiple pixel filters may be selected for AWB processing. In one embodiment, eight sets of filters, each with different configurable parameters, may be provided, and three sets of color range filters may be selected from the set for gathering tile statistics, as well as for gathering statistics for each floating window. By way of example, a first selected filter may be configured to cover the current color temperature to obtain accurate color estimation, a second selected filter may be configured to cover the low color temperature areas, and a third selected filter may be configured to cover the high color temperature areas. This particular configuration may enable the AWB algorithm to adjust the current color temperature area as the light source is changing. Further, the 2D color histogram may be utilized to determine the global and local illuminants and to determine various pixel filter thresholds for accumulating RGB values. Again, it should be understood that the selection of three pixel filters is meant to illustrate just one embodiment. In other embodiments, fewer or more pixel filters may be selected for AWB statistics. 
     Further, in addition to selecting three pixel filters, one additional pixel filter may also be used for auto-exposure (AE), which generally refers to a process of adjusting pixel integration time and gains to control the luminance of the captured image. For instance, auto-exposure may control the amount of light from the scene that is captured by the image sensor(s) by setting the integration time. In certain embodiments, tiles and floating windows of luminance statistics may be collected via the 3A statistics logic  468  and processed to determine integration and gain control parameters. 
     Further, auto-focus may refer to determining the optimal focal length of the lens in order to substantially optimize the focus of the image. In certain embodiments, floating windows of high frequency statistics may be collected and the focal length of the lens may be adjusted to bring an image into focus. As discussed further below, in one embodiment, auto-focus adjustments may utilize coarse and fine adjustments based upon one or more metrics, referred to as auto-focus scores (AF scores) to bring an image into focus. Further, in some embodiments, AF statistics/scores may be determined for different colors, and the relativity between the AF statistics/scores for each color channel may be used to determine the direction of focus. 
     Thus, these various types of statistics, among others, may be determined and collected via the statistics collection block  468 . As shown, the output STATS 0  of the statistics collection block  468  of the Sensor 0  statistics processing unit  120  may be sent to the memory  108  and routed to the control logic  84  or, alternatively, may be sent directly to the control logic  84 . Further, it should be understood that the Sensor  1  statistics processing unit  122  may also include a similarly configured 3A statistics collection block that provides statistics STATS 1 , as shown in  FIG. 8 . 
     As discussed above, the control logic  84 , which may be a dedicated processor in the ISP subsystem  32  of the device  10 , may process the collected statistical data to determine one or more control parameters for controlling the imaging device  30  and/or the image processing circuitry  32 . For instance, such the control parameters may include parameters for operating the lens of the image sensor  90  (e.g., focal length adjustment parameters), image sensor parameters (e.g., analog and/or digital gains, integration time), as well as ISP pipe processing parameters (e.g., digital gain values, color correction matrix (CCM) coefficients). Additionally, as mentioned above, in certain embodiments, statistical processing may occur at a precision of 8-bits and, thus, raw pixel data having a higher bit-depth may be down-scaled to an 8-bit format for statistics purposes. As discussed above, down-scaling to 8-bits (or any other lower-bit resolution) may reduce hardware size (e.g., area) and also reduce processing complexity, as well as allow for the statistics data to be more robust to noise (e.g., using spatial averaging of the image data). 
     With the foregoing in mind,  FIG. 51  is a block diagram depicting logic for implementing one embodiment of the 3A statistics logic  468 . As shown, the 3A statistics logic  468  may receive a signal  582  representing Bayer RGB data which, as shown in  FIG. 37 , may correspond to the output of the inverse BLC logic  466 . The 3A statistics logic  468  may process the Bayer RGB data  582  to obtain various statistics  584 , which may represent the output STATS 0  of the 3A statistics logic  468 , as shown in  FIG. 37 , or alternatively the output STATS 1  of a statistics logic associated with the Sensor 1  statistics processing unit  122 . 
     In the illustrated embodiment, for the statistics to be more robust to noise, the incoming Bayer RGB pixels  582  are first averaged by the logic  586 . For instance, the averaging may be performed in a window size of 4×4 sensor pixels consisting of four 2×2 Bayer quads (e.g., a 2×2 block of pixels representing the Bayer pattern), and the averaged red (R), green (G), and blue (B) values in the 4×4 window may be computed and converted to 8-bits, as mentioned above. This process is illustrates in more detail with respect to  FIG. 52 , which shows a 4×4 window  588  of pixels formed as four 2×2 Bayer quads  590 . Using this arrangement, each color channel includes a 2×2 block of corresponding pixels within the window  588 , and same-colored pixels may be summed and averaged to produce an average color value for each color channel within the window  588 . For instance, red pixels  594  may be averaged to obtain an average red value (R AV )  604 , and the blue pixels  596  may be averaged to obtain an average blue value (B AV )  606  within the sample  588 . With regard to averaging of the green pixels, several techniques may be utilized since the Bayer pattern has twice as many green samples as red or blue samples. In one embodiment, the average green value (G AV )  602  may be obtained by averaging just the Gr pixels  592 , just the Gb pixels  598 , or all of the Gr and Gb pixels  592  and  598  together. In another embodiment, the Gr and Gb pixels  592  and  598  in each Bayer quad  590  may be averaged, and the average of the green values for each Bayer quad  590  may be further averaged together to obtain G AV    602 . As will be appreciated, the averaging of the pixel values across pixel blocks may provide for the reduction of noise. Further, it should be understood that the use of a 4×4 block as a window sample is merely intended to provide one example. Indeed, in other embodiments, any suitable block size may be utilized (e.g., 8×8, 16×16, 32×32, etc.). 
     Thereafter, the down-scaled Bayer RGB values  610  are input to the color space conversion logic units  612  and  614 . Because some of the 3A statistics data may rely upon pixel pixels after applying color space conversion, the color space conversion (CSC) logic  612  and CSC logic  614  may be configured to convert the down-sampled Bayer RGB values  610  into one or more other color spaces. In one embodiment, the CSC logic  612  may provide for a non-linear space conversion and the CSC logic  614  may provide for a linear space conversion. Thus, the CSC logic units  612  and  614  may convert the raw image data from sensor Bayer RGB to another color space (e.g., sRGB linear , sRGB, YCbCr, etc.) that may be more ideal or suitable for performing white point estimation for white balance. 
     In the present embodiment, the non-linear CSC logic  612  may be configured to perform a 3×3 matrix multiply, followed by a non-linear mapping implemented as a lookup table, and further followed by another 3×3 matrix multiply with an added offset. This allows for the 3A statistics color space conversion to replicate the color processing of the RGB processing in the ISP pipeline  82  (e.g., applying white balance gain, applying a color correction matrix, applying RGB gamma adjustments, and performing color space conversion) for a given color temperature. It may also provide for the conversion of the Bayer RGB values to a more color consistent color space such as CIELab, or any of the other color spaces discussed above (e.g., YCbCr, a red/blue normalized color space, etc.). Under some conditions, a Lab color space may be more suitable for white balance operations because the chromaticity is more linear with respect to brightness. 
     As shown in  FIG. 51 , the output pixels from the Bayer RGB down-scaled signal  610  are processed with a first 3×3 color correction matrix (3A_CCM), referred to herein by reference number  614 . In the present embodiment, the 3A_CCM  616  may be configured to convert from a camera RGB color space (camRGB), to a linear sRGB calibrated space (sRGB linear ). A programmable color space conversion that may be used in one embodiment is provided below by Equations 19-21:
 
 sR   linear =max(0,min(255,(3 A _CCM — 00* R+ 3 A _CCM — 01* G+ 3 A _CCM — 02* B )));  (19)
 
 sG   linear =max(0,min(255,(3 A _CCM — 10* R+ 3 A _CCM — 11* G+ 3 A _CCM — 12* B )));  (20)
 
 sB   linear =max(0,min(255,(3 A _CCM — 20* R+ 3 A _CCM — 21* G+ 3 A _CCM — 22* B )));  (21)
 
wherein 3A_CCM — 00-3A_CCM — 22 represent signed coefficients of the matrix  614 . Thus, each of the sR linear , sG linear , and sB linear , components of the sRGB linear  color space may be determined first determining the sum of the red, blue, and green down-sampled Bayer RGB values with corresponding 3A_CCM coefficients applied, and then clipping this value to either 0 or 255 (the minimum and maximum pixel values for 8-bit pixel data) if the value exceeds 255 or is less than 0. The resulting sRGB linear  values are represented in  FIG. 51  by reference number  618  as the output of the 3A_CCM  616 . Additionally, the 3A statistics logic  468  may maintain a count of the number of clipped pixels for each of the sR linear , sG linear , and sB linear  components, as expressed below:
 
     3A_CCM_R_clipcount_low: number of sR linear  pixels&lt;0 clipped 
     3A_CCM_R_clipcount_high: number of sR linear  pixels&gt;255 clipped 
     3A_CCM_G_clipcount_low: number of sG linear  pixels&lt;0 clipped 
     3A_CCM_G_clipcount_high: number of sG linear  pixels&gt;255 clipped 
     3A_CCM_B_clipcount_low: number of sB linear  pixels&lt;0 clipped 
     3A_CCM_B_clipcount_high: number of sB linear  pixels&gt;255 clipped 
     Next, the sRGB linear  pixels  618  may be processed using a non-linear lookup table  620  to produce sRGB pixels  622 . The lookup table  620  may contain entries of 8-bit values, with each table entry value representing an output levels. In one embodiment, the look-up table  620  may include 65 evenly distributed input entries, wherein a table index represents input values in steps of 4. When the input value falls between intervals, the output values are linearly interpolated. 
     As will be appreciated, the sRGB color space may represent the color space of the final image produced by the imaging device  30  ( FIG. 7 ) for a given white point, as white balance statistics collection is performed in the color space of the final image produced by the image device. In one embodiment, a white point may be determined by matching the characteristics of the image scene to one or more reference illuminants based, for example, upon red-to-green and/or blue-to-green ratios. For instance, one reference illuminant may be D65, a CIE standard illuminant for simulating daylight conditions. In addition to D65, calibration of the imaging device  30  may also be performed for other different reference illuminants, and the white balance determination process may include determining a current illuminant so that processing (e.g., color balancing) may be adjusted for the current illuminant based on corresponding calibration points. By way of example, in one embodiment, the imaging device  30  and 3A statistics logic  468  may be calibrated using, in addition to D65, a cool white fluorescent (CWF) reference illuminant, the TL84 reference illuminant (another fluorescent source), and the IncA (or A) reference illuminant, which simulates incandescent lighting. Additionally, as discussed above, various other illuminants corresponding to different color temperatures (e.g., H, IncA, D50, D65, and D75, etc.) may also be used in camera calibration for white balance processing. Thus, a white point may be determined by analyzing an image scene and determining which reference illuminant most closely matches the current illuminant source. 
     Referring still to the non-linear CSC logic  612 , the sRGB pixel output  620  of the look-up table  620  may be further processed with a second 3×3 color correction matrix  624 , referred to herein as 3A_CSC. In the depicted embodiment, the 3A_CSC matrix  624  is shown as being configured to convert from the sRGB color space to the YCbCr color space, though it may be configured to convert the sRGB values into other color spaces as well. By way of example, the following programmable color space conversion (Equations 22-27) may be used:
 
 Y= 3 A _CSC — 00* sR+ 3 A _CSC — 01* sG+ 3 A _CSC — 02* sB+ 3 A _Offset Y;   (22)
 
 Y =max(3 A _CSC_MIN —   Y ,min(3 A _CSC_MAX —   Y,Y ));  (23)
 
 C 1=3 A _CSC — 10* sR+ 3 A _CSC — 11* sG+ 3 A _CSC — 12* sB+ 3 A _Offset C 1;  (24)
 
 C 1=max(3 A _CSC_MIN —   C 1,min(3 A _CSC_MAX —   C 1, C 1));  (25)
 
 C 2=3 A _CSC — 20* sR+ 3 A _CSC — 21* sG+ 3 A _CSC — 22* sB+ 3 A _Offset C 2;  (26)
 
 C 2=max(3 A _CSC_MIN —   C 2,min(3 A _CSC_MAX —   C 2, C 2));  (27)
 
wherein 3A_CSC — 00-3A_CSC — 22 represent signed coefficients for the matrix  624 , 3A_OffsetY, 3A_OffsetC1, and 3A_OffsetC2 represent signed offsets, and C1 and C2 represent different colors, here blue-difference chroma (Cb) and red-difference chroma (Cr), respectively. It should be understood, however, that C1 and C2 may represent any suitable difference chroma colors, and need not necessarily be Cb and Cr colors.
 
     As shown in Equations 22-27, in determining each component of YCbCr, appropriate coefficients from the matrix  624  are applied to the sRGB values  622  and the result is summed with a corresponding offset (e.g., Equations 22, 24, and 26). Essentially, this step is a 3×1 matrix multiplication step. This result from the matrix multiplication is then clipped between a maximum and minimum value (e.g., Equations 23, 25, and 27). The associated minimum and maximum clipping values may be programmable and may depend, for instance, on particular imaging or video standards (e.g., BT.601 or BT.709) being utilized. 
     The 3A statistics logic  468  may also maintain a count of the number of clipped pixels for each of the Y, C1, and C2 components, as expressed below: 
     3A_CSC_Y_clipcount_low: number of Y pixels&lt;3A_CSC_MIN_Y clipped 
     3A_CSC_Y_clipcount_high: number of Y pixels&gt;3A_CSC_MAX_Y clipped 
     3A_CSC_C1_clipcount_low: number of C1 pixels&lt;3A_CSC_MIN_C1 clipped 
     3A_CSC_C1_clipcount_high: number of C1 pixels&gt;3A_CSC_MAX_C1 clipped 
     3A_CSC_C2_clipcount_low: number of C2 pixels&lt;3A_CSC_MIN_C2 clipped 
     3A_CSC_C2_clipcount_high: number of C2 pixels&lt;3A_CSC_MAX_C2 clipped 
     The output pixels from the Bayer RGB down-sample signal  610  may also be provided to the linear color space conversion logic  614 , which may be configured to implement a camera color space conversion. For instance, the output pixels  610  from the Bayer RGB down-sample logic  586  may be processed via another 3×3 color conversion matrix (3A_CSC2)  630  of the CSC logic  614  to convert from sensor RGB (camRGB) to a linear white-balanced color space (camYC1C2), wherein C1 and C2 may correspond to Cb and Cr, respectively. In one embodiment, the chroma pixels may be scaled by luma, which may be beneficial in implementing a color filter that has improved color consistency and is robust to color shifts due to luma changes. An example of how the camera color space conversion may be performed using the 3×3 matrix  630  is provided below in Equations 28-31:
 
cam Y= 3 A _CSC2 — 00* R+ 3 A _CSC2 — 01* G+ 3 A _CSC2 — 02* B+ 3 A _Offset2 Y;   (28)
 
cam Y =max(3 A _CSC2_MIN —   Y ,min(3 A _CSC2_MAX —   Y ,cam Y ));  (29)
 
cam C 1=(3 A _CSC2 — 10* R+ 3 A _CSC2 — 11* G+ 3 A _CSC2 — 12* B );  (30)
 
cam C 2=(3 A _CSC2 — 20* R+ 3 A _CSC2 — 21* G+ 3 A _CSC2 — 22* B );  (31)
 
wherein 3A_CSC2 — 00-3A_CSC2 — 22 represent signed coefficients for the matrix  630 , 3A_Offset2Y represents a signed offset for camY, and camC1 and camC2 represent different colors, here blue-difference chroma (Cb) and red-difference chroma (Cr), respectively. As shown in Equation 28, to determine camY, corresponding coefficients from the matrix  630  are applied to the bayer RGB values  610 , and the result is summed with 3A_Offset2Y. This result is then clipped between a maximum and minimum value, as shown in Equation 29. As discussed above, the clipping limits may be programmable.
 
     At this point, the camC1 and camC2 pixels of the output  632  are signed. As discussed above, in some embodiments, chroma pixels may be scaled. For example, one technique for implementing chroma scaling is shown below:
 
cam C 1=cam C 1*ChromaScale*255/(cam Y ?cam Y: 1);  (32)
 
cam C 2=cam C 2*ChromaScale*255/(cam Y ?cam Y: 1);  (33)
 
wherein ChromaScale represents a floating point scaling factor between 0 and 8. In Equations 32 and 33, the expression (camY ?camY:1) is meant to prevent a divide-by-zero condition. That is, if camY is equal to zero, the value of camY is set to 1. Further, in one embodiment, ChromaScale may be set to one of two possible values depending on the sign of camC1. For instance, as shown below in Equation 34, ChomaScale may be set to a first value (ChromaScale 0 ) if camC1 is negative, or else may be set to a second value (ChromaScale 1 ):
 
     
       
         
           
             
               
                 
                   ChromaScale 
                   = 
                   
                     
                       
                         
                           ChromaScale 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           0 
                         
                       
                       
                         
                           if 
                           ⁡ 
                           
                             ( 
                             
                               
                                 camC 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 1 
                               
                               &lt; 
                               0 
                             
                             ) 
                           
                         
                       
                     
                     
                       
                         
                           ChromaScale 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           1 
                         
                       
                       
                         otherwise 
                       
                     
                   
                 
               
               
                 
                   ( 
                   34 
                   ) 
                 
               
             
           
         
       
     
     Thereafter, chroma offsets are added, and the camC1 and camC2 chroma pixels are clipped, as shown below in Equations 35 and 36, to generate corresponding unsigned pixel values:
 
cam C 1=max(3 A _CSC2_MIN —   C 1,min(3 A _CSC2_MAX —   C 1,(cam C 1+3 A _Offset2 C 1)))  (35)
 
cam C 2=max(3 A _CSC2_MIN —   C 2,min(3 A _CSC2_MAX —   C 2,(cam C 2+3 A _Offset2 C 2)))  (36)
 
wherein 3A_CSC2 — 00-3A_CSC2 — 22 are signed coefficients of the matrix  630 , and 3A_Offset2C1 and 3A_Offset2C2 are signed offsets. Further, the number of pixels that are clipped for camY, camC1, and camC2 are counted, as shown below:
 
     3A_CSC2_Y_clipcount_low: number of camY pixels&lt;3A_CSC2_MIN_Y clipped 
     3A_CSC2_Y_clipcount_high: number of camY pixels&gt;3A_CSC2_MAX_Y clipped 
     3A_CSC2_C1_clipcount_low: number of camC1 pixels&lt;3A_CSC2_MIN_C1 clipped 
     3A_CSC2_C1_clipcount_high: number of camC1 pixels&gt;3A_CSC2_MAX_C1 clipped 
     3A_CSC2_C2_clipcount_low: number of camC2 pixels&lt;3A_CSC2_MIN_C2 clipped 
     3A_CSC2_C2_clipcount_high: number of camC2 pixels&gt;3A_CSC2_MAX_C2 clipped 
     Thus, the non-linear and linear color space conversion logic  612  and  614  may, in the present embodiment, provide pixel data in various color spaces: sRGB linear  (signal  618 ), sRGB (signal  622 ), YCbYr (signal  626 ), and camYCbCr (signal  630 ). It should be understood that the coefficients for each conversion matrix  616  (3A_CCM),  624  (3A_CSC), and  630  (3A_CSC2), as well as the values in the look-up table  620 , may be independently set and programmed 
     Referring still to  FIG. 51 , the chroma output pixels from either the non-linear color space conversion (YCbCr  626 ) or the camera color space conversion (camYCbCr  632 ) may be used to generate a two-dimensional (2D) color histogram  636 . As shown, selection logic  638  and  640 , which may be implemented as multiplexers or by any other suitable logic, may be configured to select between luma and chroma pixels from either the non-linear or camera color space conversion. The selection logic  638  and  640  may operate in response to respective control signals which, in one embodiment, may be supplied by the main control logic  84  of the image processing circuitry  32  ( FIG. 7 ) and may be set via software. 
     For the present example, it may be assumed that the selection logic  638  and  640  select the YC1C2 color space conversion ( 626 ), where the first component is Luma, and where C1, C2 are the first and second colors (e.g., Cb, Cr). A 2D histogram  636  in the C1-C2 color space is generated for one window. For instance, the window may be specified with a column start and width, and a row start and height. In one embodiment, the window position and size may be set as a multiple of 4 pixels, and 32×32 bins may be used for a total of 1024 bins. The bin boundaries may be at fixed interval and, in order to allow for zooming and panning of the histogram collection in specific areas of the color space, a pixel scaling and offset may defined. 
     The upper 5 bits (representing a total of 32 values) of C1 and C2 after offset and scaling may used to determine the bin. The bin indices for C1 and C2, referred to herein by C1 index and C2 index, may be determined as follows:
 
 C 1_index=(( C 1− C 1_offset)&gt;&gt;(3− C 1_scale)  (37)
 
 C 2_index=(( C 2− C 2_offset)&gt;&gt;(3− C 2_scale)  (38)
 
Once the indices are determined, the color histogram bins are incremented by a Count value (which may have a value of between 0 and 3 in one embodiment) if the bin indices are in the range [0, 31], as shown below in Equation 39. Effectively, this allows for weighting the color counts based on luma values (e.g., brighter pixels are weighted more heavily, instead of weighting everything equally (e.g., by 1)).
 
if( C 1_index&gt;=0&amp;&amp; C 1_index&lt;=31&amp;&amp; C 2_index&gt;=0&amp;&amp; C 2_index&lt;=31) StatsCbCrHist[ C 2_index&amp;31][ C 1_index&amp;31]+=Count;   (39)
 
where Count is determined based on the selected luma value, Y in this example. As will be appreciated, the steps represented by Equations 37, 38, and 39 may be implemented by a bin update logic block  644 . Further, in one embodiment, multiple luma thresholds may be set to define luma intervals. By way of example, four luma thresholds (Ythd 0 -Ythd 3 ) may define five luma intervals, with Count values Count 0 - 4  being defined for each interval. For instance, Count 0 -Count 4  may be selected (e.g., by pixel condition logic  642 ) based on luma thresholds as follows:
 
if ( Y&lt;=Ythd 0)
 
Count=Count0
 
else if ( Y&lt;=Ythd 1)
 
Count=Count1
 
else if ( Y&lt;=Ythd 2)
 
Count=Count2
 
else if ( Y&lt;=Ythd 3)
 
Count=Count3
 
else
 
Count=Count4  (40)
 
     With the foregoing in mind,  FIG. 53  illustrates the color histogram with scaling and offsets set to zero for both C1 and C2. The divisions within the CbCr space represent each of the 32×32 bins (1024 total bins).  FIG. 54  provides an example of zooming and panning within the 2D color histogram for additional precision, wherein the rectangular area  646  where the small rectangle specifies the location of the 32×32 bins. 
     At the start of a frame of image data, bin values are initialized to zero. For each pixel going into the 2D color histogram  636 , the bin corresponding to the matching C1C2 value is incremented by a determined Count value (Count 0 -Count 4 ) which, as discussed above, may be based on the luma value. For each bin within the 2D histogram  636 , the total pixel count is reported as part of the collected statistics data (e.g., STATS 0 ). In one embodiment, the total pixel count for each bin may have a resolution of 22-bits, whereby an allocation of internal memory equal to 1024×22 bits is provided. 
     Referring back to  FIG. 51 , the Bayer RGB pixels (signal  610 ), sRGB linear  pixels (signal  618 ), sRGB pixels (signal  622 ), and YC1C2 (e.g., YCbCr) pixels (signal  626 ) are provided to a set of pixel filters  650   a - c , where by RGB, sRGB linear , sRGB, YC1C2, or camYC1C2 sums may be accumulated conditionally upon either camYC1C2 or YC pixel conditions, as defined by each pixel filter  650 . That is, Y, C1 and C2 values from either output of the non-linear color space conversion (YC1C2) or the output of the camera color space conversion (camYC1C2) are used to conditionally select RGB, sRGB linear , sRGB or YC1C2 values to accumulate. While the present embodiment depicts the 3A statistics logic  468  as having 8 pixel filters (PF 0 -PF 7 ) provided, it should be understood that any number of pixel filters may be provided. 
       FIG. 55  shows a functional logic diagram depicting an embodiment of the pixel filters, specifically PF 0  ( 650   a ) and PF 1  ( 650   b ) from  FIG. 51 . As shown, each pixel filter  650  includes a selection logic, which receives the Bayer RGB pixels, the sRGB linear  pixels, the sRGB pixels, and one of either the YC1C2 or camYC1C2 pixels, as selected by another selection logic  654 . By way of example, the selection logic  652  and  654  may be implemented using multiplexers or any other suitable logic. The selection logic  654  may select either YC1C2 or camYC1C2. The selection may be made in response to a control signal which may be supplied by the main control logic  84  of the image processing circuitry  32  ( FIG. 7 ) and/or set by software. Next, the pixel filter  650  may use logic  656  to evaluate the YC1C2 pixels (e.g., either non-linear or camera) selected by the selection logic  654  against a pixel condition. Each pixel filter  650  may use the selection circuit  652  to select one of either the Bayer RGB pixels, sRGB linear  pixels, sRGB pixels, and YC1C2 or camYC1C2 pixel depending on the output from the selection circuit  654 . 
     Using the results of the evaluation, the pixels selected by the selection logic  652  may be accumulated. In one embodiment, the pixel condition may be defined using thresholds C1_min, C1_max, C2_min, C2_max, as shown in graph  570  of  FIG. 49 . A pixel is included in the statistics if it satisfies the following conditions: 
     1. C1_min&lt;=C1&lt;=C1_max 
     2. C2_min&lt;=C2&lt;=C2_max 
     3. abs((C2_delta*C1)−(C1_delta*C2)+Offset)&lt;distance_max 
     4. Y min &lt;=Y&lt;=Y max    
     Referring to graph  FIG. 56 , in one embodiment, the point  662  represents the values (C2, C1) corresponding to the current YC1C2 pixel data, as selected by the logic  654 . C1_delta may be determined as the difference between C1 — 1 and C1 — 0, and C2_delta may be determined as the difference between C2 — 1 and C2 — 0. As shown in  FIG. 56 , the points (C1 — 0, C2 — 0) and (C1 — 1, C2 — 1) may define the minimum and maximum boundaries for C1 and C2. The Offset may be determined by multiplying C1_delta by the value (C2_intercept) at where the line  664  intercepts the axis C2. Thus, assuming that Y, C1, and C2 satisfy the minimum and maximum boundary conditions, the selected pixels (Bayer RGB, sRGB linear , sRGB, and YC1C2/camYC1C2) is included in the accumulation sum if its distance  670  from the line  664  is less than distance_max  672 , which may be distance  670  in pixels from the line multiplied by a normalization factor:
 
distance_max=distance*sqrt( C 1_delta^2+ C 2_delta^2)
 
In the present embodiment, distance, C1_delta and C2_delta may have a range of −255 to 255. Thus, distance_max  672  may be represented by 17 bits. The points (C1 — 0, C2 — 0) and (C1 — 1, C2 — 1), as well as parameters for determining distance_max (e.g., normalization factor(s)), may be provided as part of the pixel condition logic  656  in each pixel filter  650 . As will be appreciated, the pixel conditions  656  may be configurable/programmable.
 
     While the example shown in  FIG. 56  depicts a pixel condition based on two sets of points (C1 — 0, C2 — 0) and (C1 — 1, C2 — 1), in additional embodiments, certain pixel filters may define more complex shapes and regions upon which pixel conditions are determined. For instance,  FIG. 57  shows an embodiment where a pixel filter  650  may define a five-sided polygon  673  using points (C1 — 0, C2 — 0), (C1 — 1, C2 — 1), (C1 — 2, C2 — 2) and (C1 — 3, C2 — 3), and (C1 — 4, C2 — 4). Each side  674   a - 674   e  may define a line condition. However, unlike the case shown in  FIG. 56  (e.g., the pixel may be on either side of line  664  as long as distance_max is satisfied), the condition may be that the pixel (C1, C2) must be located on the side of the line  674   a - 674   e  such that it is enclosed by the polygon  673 . Thus, the pixel (C1, C2) is counted when the intersection of multiple line conditions is met. For instance, in  FIG. 57 , such an intersection occurs with respect to pixel  675   a . However, pixel  675   b  fails to satisfy the line condition for line  674   d  and, therefore, would not be counted in the statistics when processed by a pixel filter configured in this manner. 
     In a further embodiment, shown in  FIG. 58 , a pixel condition may be determined based on overlapping shapes. For instance,  FIG. 58  shows how a pixel filter  650  may have pixel conditions defined using two overlapping shapes, here rectangles  676   a  and  676   b  defined by points (C1 — 0, C2 — 0), (C1 — 1, C2 — 1), (C1 — 2, C2 — 2) and (C1 — 3, C2 — 3) and points (C1 — 4, C2 — 4), (C1 — 5, C2 — 5), (C1 — 6, C2 — 6) and (C1 — 7, C2 — 7), respectively. In this example, a pixel (C1, C2) may satisfy line conditions defined by such a pixel filter by being enclosed within the region collectively bounded by the shapes  676   a  and  676   b  (e.g., by satisfying the line conditions of each line defining both shapes). For instance, in  FIG. 58 , these conditions are satisfied with respect to pixel  678   a . However, pixel  678   b  fails to satisfy these conditions (specifically with respect to line  679   a  of rectangle  676   a  and line  679   b  of rectangle  676   b ) and, therefore, would not be counted in the statistics when processed by a pixel filter configured in this manner. 
     For each pixel filter  650 , qualifying pixels are identified based on the pixel conditions defined by logic  656  and, for qualifying pixel values, the following statistics may be collected by the 3A statistics engine  468 : 32-bit sums: (R sum , G sum , B sum ) or (sR linear     —     sum , sG linear     —     sum , sB linear     —     sum ), or (sR sum , sG sum , sB sum ) or (Y sum , C1 sum , C2 sum ) and a 24-bit pixel count, Count, which may represent the sum of the number of pixels that were included in the statistic. In one embodiment, software may use the sum to generate an average in within a tile or window. 
     When the camYC1C2 pixels are selected by logic  652  of a pixel filter  650 , color thresholds may be performed on scaled chroma values. For instance, since chroma intensity at the white points increases with luma value, the use of chroma scaled with the luma value in the pixel filter  650  may, in some instances, provide results with improved consistency. For example, minimum and maximum luma conditions may allow the filter to ignore dark and/or bright areas. If the pixel satisfies the YC1C2 pixel condition, the RGB, sRGB linear , sRGB or YC1C2 values are accumulated. The selection of the pixel values by the selection logic  652  may depend on the type of information needed. For instance, for white balance, typically RGB or sRGB linear  pixels are selected. For detecting specific conditions, such as sky, grass, skin tones, etc., a YCC or sRGB pixel set may be more suitable. 
     In the present embodiment, eight sets of pixel conditions may be defined, one associated with each of the pixel filters PF 0 -PF 7   650 . Some pixel conditions may be defined to carve an area in the C1-C2 color space ( FIG. 49 ) where the white point is likely to be. This may be determined or estimated based on the current illuminant. Then, accumulated RGB sums may be used to determine the current white point based on the R/G and/or B/G ratios for white balance adjustments. Further, some pixel conditions may be defined or adapted to perform scene analysis and classifications. For example, some pixel filters  650  and windows/tiles may be utilized to detect for conditions, such as blue sky in a top portion of an image frame, or green grass in a bottom portion of an image frame. This information can also be used to adjust white balance. Additionally, some pixel conditions may be defined or adapted to detect skin tones. For such filters, tiles may be used to detect areas of the image frame that have skin tone. By identifying these areas, the quality of skin tone may be improved by, for example, reducing the amount of noise filter in skin tone areas and/or decreasing the quantization in the video compression in those areas to improve quality. 
     The 3A statistics logic  468  may also provide for the collection of luma data. For instance, the luma value, camY, from the camera color space conversion (camYC1C2) may be used for accumulating luma sum statistics. In one embodiment, the following luma information is may be collected by the 3A statistics logic  468 : 
     Y sum : sum of camY 
     cond(Y sum ): sum of camY that satisfies the condition: Y min &lt;=camY&lt;Y max    
     Ycount 1 : count of pixels where camY&lt;Y min , 
     Ycount 2 : count of pixels where camY&gt;=Y max    
     Here, Ycount 1  may represent the number of underexposed pixels and Ycount 2  may represent the number of overexposed pixels. This may be used to determine whether the image is overexposed or underexposed. For instance, if the pixels do not saturate, the sum of camY (Y− sum ) may indicate average luma in a scene, which may be used to achieve a target AE exposure. For instance, in one embodiment, the average luma may be determined by dividing Y sum  by the number of pixels. Further, by knowing the luma/AE statistics for tile statistics and window locations, AE metering may be performed. For instance, depending on the image scene, it may be desirable to weigh AE statistics at the center window more heavily than those at the edges of the image, such as may be in the case of a portrait. 
     In the presently illustrated embodiment, the 3A statistics collection logic may be configured to collect statistics in tiles and windows. In the illustrated configuration, one window may be defined for tile statistics  674 . The window may be specified with a column start and width, and a row start and height. In one embodiment, the window position and size may be selected as a multiple of four pixels and, within this window, statistics are gathered in tiles of arbitrary sizes. By way of example, all tiles in the window may be selected such that they have the same size. The tile size may be set independently for horizontal and vertical directions and, in one embodiment, the maximum limit on the number of horizontal tiles may be set (e.g., a limit of 128 horizontal tiles). Further, in one embodiment, the minimum tile size may be set to 8 pixels wide by 4 pixels high, for example. Below are some examples of tile configurations based on different video/imaging modes and standards to obtain a window of 16×16 tiles: 
     VGA 640×480: the interval 40×30 pixels 
     HD1280×720: the interval 80×45 pixels 
     HD1920×1080: the interval 120×68 pixels 
     5 MP 2592×1944: the interval 162×122 pixels 
     8 MP 3280×2464: the interval 205×154 pixels 
     With regard to the present embodiment, from the eight available pixel filters  650  (PF 0 -PF 7 ), four may be selected for tile statistics  674 . For each tile, the following statistics may collected:
         (R sum0 , G sum0 , B sum0 ) or (sR linear     —     sum0 , sG linear     —     sum0 , sB linear     —     sum0 ), or (sR sum0 , sG sum0 , sB sum0 ) or (Y sum0 , C1 sum0 , C2 sum0 ), Count 0     (R sum1 , G sum1 , B sum1 ) or (sR linear     —     sum1 , sG linear     —     sum1 , sB linear     —     sum1 ), or (sR sum1 , sG sum1 , sB sum1 ) or (Y sum1 , C1 sum1 , C2 sum1 ), Count 1     (R sum2 , G sum2 , B sum2 ) or (sR linear     —     sum2 , sG linear     —     sum2 , sB linear     —     sum2 ), or (sR sum2 , sG sum2 , sB sum2 ) or (Y sum2 , C1 sum2 , C2 sum2 ), Count 2     (R sum3 , G sum3 , B sum3 ) or (sR linear     —     sum3 , sG linear     —     sum3 , sB linear     —     sum3 ), or (sR sum3 , sG sum3 , sB sum3 ) or (Y sum3 , C1 sum3 , C2 sum3 ), Count 3     Y sum , cond(Y sum ), Y count1 , Y count2  (from camY)
 
In the above-listed statistics, Count 0 - 3  represents the count of pixels that satisfy pixel conditions corresponding to the selected four pixel filters. For example, if pixel filters PF 0 , PF 1 , PF 5 , and PF 6  are selected as the four pixel filters for a particular tile or window, then the above-provided expressions may correspond to the Count values and sums corresponding to the pixel data (e.g., Bayer RGB, sRGB linear , sRGB, YC1Y2, camYC1C2) which is selected for those filters (e.g., by selection logic  652 ). Additionally, the Count values may be used to normalize the statistics (e.g., by dividing color sums by the corresponding Count values). As shown, depending at least partially upon the types of statistics needed, the selected pixels filters  650  may be configured to select between either one of Bayer RGB, sRGB linear , or sRGB pixel data, or YC1C2 (non-linear or camera color space conversion depending on selection by logic  654 ) pixel data, and determine color sum statistics for the selected pixel data. Additionally, as discussed above the luma value, camY, from the camera color space conversion (camYC1C2) is also collected for luma sum information for auto-exposure (AE) statistics.
       

     Additionally, the 3A statistics logic  468  may also be configured to collect statistics  676  for multiple windows. For instance, in one embodiment, up to eight floating windows may be used, with any rectangular region having a multiple of four pixels in each dimension (e.g., height×width), up to a maximum size corresponding to the size of the image frame. However, the location of the windows is not necessarily restricted to multiples of four pixels. For instance, windows can overlap with one another. 
     In the present embodiment, four pixel filters  650  may be selected from the available eight pixel filters (PF 0 -PF 7 ) for each window. Statistics for each window may be collected in the same manner as for tiles, discussed above. Thus, for each window, the following statistics  676  may be collected:
         (R sum0 , G sum0 , B sum0 ) or (sR linear     —     sum0 , sG linear     —     sum0 , sB linear     —     sum0 ), or (sR sum0 , sG sum0 , sB sum0 ) or (Y sum0 , C1 sum0 , C2 sum0 ), Count 0     (R sum1 , G sum1 , B sum1 ) or (sR linear     —     sum1 , sG linear     —     sum1 , sB linear     —     sum1 ), or (sR sum1 , sG sum1 , sB sum1 ) or (Y sum1 , C1 sum1 , C2 sum1 ), Count 1     (R sum2 , G sum2 , B sum2 ) or (sR linear     —     sum2 , sG linear     —     sum2 , sB linear     —     sum2 ), or (sR sum2 , sG sum2 , sB sum2 ) or (Y sum2 , C1 sum2 , C2 sum2 ), Count 2     (R sum3 , G sum3 , B sum3 ) or (sR linear     —     sum3 , sG linear     —     sum3 , sB linear     —     sum3 ), or (sR sum3 , sG sum3 , sB sum3 ) or (Y sum3 , C1 sum3 , C2 sum3 ), Count 3     Y sum , cond(Y sum ), Y count1 , Y count2  (from camY)
 
In the above-listed statistics, Count 0 - 3  represents the count of pixels that satisfy pixel conditions corresponding to the selected four pixel filters for a particular window. From the eight available pixel filters PF 0 -PF 7 , the four active pixel filters may be selected independently for each window. Additionally, one of the sets of statistics may be collected using pixel filters or the camY luma statistics. The window statistics collected for AWB and AE may, in one embodiment, be mapped to one or more registers.
       

     Referring still to  FIG. 51 , the 3A statistics logic  468  may also be configured to acquire luma row sum statistics  678  for one window using the luma value, camY, for the camera color space conversion. This information may be used to detect and compensate for flicker. Flicker is generated by a periodic variation in some fluorescent and incandescent light sources, typically caused by the AC power signal. For example, referring to  FIG. 59 , a graph illustrating how flicker may be caused by variations in a light source is shown. Flicker detection may thus be used to detect the frequency of the AC power used for the light source (e.g., 50 Hz or 60 Hz). Once the frequency is known, flicker may be avoided by setting the image sensor&#39;s integration time to an integer multiple of the flicker period. 
     To detect for flicker, the camera luma, camY, is accumulated over each row. Due to the down-sample of the incoming Bayer data, each camY value may corresponds to 4 rows of the original raw image data. Control logic and/or firmware may then perform a frequency analysis of the row average or, more reliably, of the row average differences over consecutive frames to determine the frequency of the AC power associated with a particular light source. For example, with respect to  FIG. 59 , integration times for the image sensor may be based on times t 1 , t 2 , t 3 , and t 4  (e.g., such that integration occurs at times corresponding to when a lighting source exhibiting variations is generally at the same brightness level. 
     In one embodiment, a luma row sum window may be specified and statistics  678  are reported for pixels within that window. By way of example, for 1080p HD video capture, assuming a window of 1024 pixel high, 256 luma row sums are generated (e.g., one sum for every four rows due to downscaling by logic  586 ), and each accumulated value may be expressed with 18 bits (e.g., 8-bit camY values for up to 1024 samples per row). 
     The 3A statistics collection logic  468  of  FIG. 51  may also provide for the collection of auto-focus (AF) statistics  682  by way of the auto-focus statistics logic  680 . A functional block diagram showing an embodiment of the AF statistics logic  680  in more detail is provided in  FIG. 60 . As shown, the AF statistics logic  680  may include a horizontal filter  684  and an edge detector  686  which is applied to the original Bayer RGB (not down-sampled), two 3×3 filters  688  on Y from Bayer, and two 3×3 filters  690  on camY. In general, the horizontal filter  684  provides a fine resolution statistics per color component, the 3×3 filters  688  may provide fine resolution statistics on BayerY (Bayer RGB with 3×1 transform (logic  687 ) applied), and the 3×3 filters  690  may provide coarser two-dimensional statistics on camY (since camY is obtained using down-scaled Bayer RGB data, i.e., logic  630 ). Further, the logic  680  may include logic  704  for decimating the Bayer RGB data (e.g., 2×2 averaging, 4×4 averaging, etc.), and the decimated Bayer RGB data  705  may be filtered using 3×3 filters  706  to produce a filtered output  708  for decimated Bayer RGB data. The present embodiment provides for 16 windows of statistics. At the raw frame boundaries, edge pixels are replicated for the filters of the AF statistics logic  680 . The various components of the AF statistics logic  680  are described in further detail below. 
     First, the horizontal edge detection process includes applying the horizontal filter  684  for each color component (R, Gr, Gb, B) followed by an optional edge detector  686  on each color component. Thus, depending on imaging conditions, this configuration allows for the AF statistic logic  680  to be set up as a high pass filter with no edge detection (e.g., edge detector disabled) or, alternatively, as a low pass filter followed by an edge detector (e.g., edge detector enabled). For instance, in low light conditions, the horizontal filter  684  may be more susceptible to noise and, therefore, the logic  680  may configure the horizontal filter as a low pass filter followed by an enabled edge detector  686 . As shown, the control signal  694  may enable or disable the edge detector  686 . The statistics from the different color channels are used to determine the direction of the focus to improve sharpness, since the different colors may focus at different depth. In particular, the AF statistics logic  680  may provide for techniques to enabling auto-focus control using a combination of coarse and fine adjustments (e.g., to the focal length of the lens). Embodiments of such techniques are described in additional detail below. 
     In one embodiment the horizontal filter may be a 7-tap filter and may be defined as follows in Equations 41 and 42:
 
out( i )=( af _horzfilt_coeff [0]*(in( i− 3)+in( i+ 3))+ af _horzfilt_coeff [1]*(in( i− 2)+in( i+ 2))+ af _horzfilt_coeff [2]*(in( i− 1)+in( i+ 1))+ af _horzfilt_coeff [3]*in( i ))  (41)
 
out( i )=max(−255,min(255,out( i )))  (42)
 
Here, each coefficient af_horzfilt_coeff [0:3] may be in the range [−2, 2], and i represents the input pixel index for R, Gr, Gb or B. The filtered output out(i) may be clipped between a minimum and maximum value of −255 and 255, respectively (Equation 42). The filter coefficients may be defined independently per color component.
 
     The optional edge detector  686  may follow the output of the horizontal filter  684 . In one embodiment, the edge detector  686  may be defined as:
 
edge( i )=abs(−2*out( i− 1)+2*out( i+ 1))+abs(−out( i− 2)+out( i+ 2))  (43)
 
edge( i )=max(0,min(255,edge( i )))  (44)
 
Thus, the edge detector  686 , when enabled, may output a value based upon the two pixels on each side of the current input pixel i, as depicted by Equation 43. The result may be clipped to an 8-bit value between 0 and 255, as shown in Equation 44.
 
     Depending on whether an edge is detected, the final output of the pixel filter (e.g., filter  684  and detector  686 ) may be selected as either the output of the horizontal filter  684  or the output of the edge detector  686 . For instance, as shown in Equation 45, the output of the edge detector  686  may be edge(i) if an edge is detected, or may be the absolute value of the horizontal filter output out(i) if no edge is detected.
 
edge( i )=( af _horzfilt_edge_detected)?edge( i ):abs(out( i ))  (45)
 
For each window the accumulated values, edge_sum[R, Gr, Gb, B], may be selected to be either (1) the sum of edge(j,i) for each pixel over the window, or (2) the maximum value of edge(i) across a line in the window, max(edge), summed over the lines in the window. Assuming a raw frame size of 4096×4096 pixels, the number of bits required to store the maximum values of edge_sum[R, Gr, Gb, B] is 30 bits (e.g., 8 bits per pixel, plus 22 bits for a window covering the entire raw image frame).
 
     As discussed, the 3×3 filters  690  for camY luma may include two programmable 3×3 filters, referred to as F 0  and F 1 , which are applied to camY. The result of the filter  690  goes to either a squared function or an absolute value function. The result is accumulated over a given AF window for both 3×3 filters F 0  and F 1  to generate a luma edge value. In one embodiment, the luma edge values at each camY pixel are defined as follows:
 
edgecam Y   —   FX ( j,i )= FX *cam Y=FX (0,0)*cam Y ( j− 1, i− 1)+ FX (0,1)*cam Y ( j− 1, i )+ FX (0,2)*cam Y ( j− 1, i+ 1)+ FX (1,0)*cam Y ( j,i− 1)+ FX (1,1)*cam Y ( j,i )+ FX (1,2)*cam Y ( j,i+ 1)+ FX (2,0)*cam Y ( j+ 1, i− 1)+ FX (2,1)*cam Y ( j+ 1, i )+ FX (2,2)*cam Y ( j+ 1, i+ 1)  (46)
 
edgecam Y   —   FX ( j,i )= f (max(−255,min(255,edgecam Y   —   FX ( j,i ))))
 
 f ( a )= a^ 2 or abs( a )  (47)
 
where FX represents the 3×3 programmable filters, F 0  and F 1 , with signed coefficients in the range [−4, 4]. The indices j and i represent pixel locations in the camY image. As discussed above, the filter on camY may provide coarse resolution statistics, since camY is derived using down-scaled (e.g., 4×4 to 1) Bayer RGB data. For instance, in one embodiment, the filters F 0  and F 1  may be set using a Scharr operator, which offers improved rotational symmetry over a Sobel operator, an example of which is shown below:
 
     
       
         
           
             
               F 
               ⁢ 
               
                   
               
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     For each window, the accumulated values  700  determined by the filters  690 , edgecamY_FX_sum (where FX=F 0  and F 1 ), can selected to be either (1) the sum of edgecamY_FX(j,i) for each pixel over the window, or (2) the maximum value of edgecamY_FX(j) across a line in the window, summed over the lines in the window. In one embodiment, edgecamY_FX_sum may saturate to a 32-bit value when f(a) is set to a^2 to provide “peakier” statistics with a finer resolution. To avoid saturation, a maximum window size X*Y in raw frame pixels may be set such that it does not exceed a total of 1024×1024 pixels (e.g., i.e. X*Y&lt;=1048576 pixels). As noted above, f(a) may also be set as an absolute value to provide more linear statistics. 
     The AF 3×3 filters  688  on Bayer Y may defined in a similar manner as the 3×3 filters in camY, but they are applied to luma values Y generated from a Bayer quad (2×2 pixels). First, 8-bit Bayer RGB values are converted to Y with programmable coefficients in the range [0, 4] to generate a white balanced Y value, as shown below in Equation 48:
 
bayer Y =max(0,min(255,bayer Y _Coeff [0]* R +bayer Y _Coeff [1]*( Gr+Gb )/2+bayer Y _Coeff [2]* B ))  (48)
 
     Like the filters  690  for camY, the 3×3 filters  688  for bayerY luma may include two programmable 3×3 filters, referred to as F 0  and F 1 , which are applied to bayerY. The result of the filter  688  goes to either a squared function or an absolute value function. The result is accumulated over a given AF window for both 3×3 filters F 0  and F 1  to generate a luma edge value. In one embodiment, the luma edge values at each bayerY pixel are defined as follows:
 
edgebayer Y   —   FX ( j,i )= FX *bayer Y=FX (0,0)*bayer Y ( j− 1, i− 1)+ FX (0,1)*bayer Y ( j− 1, i )+ FX (0,2)*bayer Y ( j− 1, i )+ FX (1,0)*bayer Y ( j,i− 1)+ FX (1,1)*bayer Y ( j,i )+ FX (1,2)*bayer Y ( j− 1, i )+ FX (2,0)*bayer Y ( j+ 1, i− 1)+ FX (2,1)*bayer Y ( j+ 1, i )+ FX (2,2)*bayer Y ( j+ 1, i )  (49)
 
edgebayer Y   —   FX ( j,i )= f (max(−255,min(255,edgebayer Y   —   FX ( j,i ))))
 
 f ( a )= a^ 2 or abs( a )  (50)
 
where FX represents the 3×3 programmable filters, F 0  and F 1 , with signed coefficients in the range [−4, 4]. The indices j and i represent pixel locations in the bayerY image. As discussed above, the filter on Bayer Y may provide fine resolution statistics, since the Bayer RGB signal received by the AF logic  680  is not decimated. By way of examples only, the filters F 0  and F 1  of the filter logic  688  may be set using one of the following filter configurations:
 
     
       
         
           
             
               
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     For each window, the accumulated values  702  determined by the filters  688 , edgebayerY_FX_sum (where FX=F 0  and F 1 ), can selected to be either (1) the sum of edgebayerY_FX(j,i) for each pixel over the window, or (2) the maximum value of edgebayerY_FX(j) across a line in the window, summed over the lines in the window. Here, edgebayerY_FX_sum may saturates to 32-bits when f(a) is set to a^2. Thus, to avoid saturation, the maximum window size X*Y in raw frame pixels should be set such that it does not exceed a total of 512×512 pixels (e.g., X*Y&lt;=262144). As discussed above, setting f(a) to a^2 may provide for peakier statistics, while setting f(a) to abs(a) may provide for more linear statistics. 
     As discussed above, statistics  682  for AF are collected for 16 windows. The windows may be any rectangular area with each dimension being a multiple of 4 pixels. Because each filtering logic  688  and  690  includes two filters, in some instances, one filter may be used for normalization over 4 pixels, and may be configured to filter in both vertical and horizontal directions. Further, in some embodiments, the AF logic  680  may normalize the AF statistics by brightness. This may be accomplished by setting one or more of the filters of the logic blocks  688  and  690  as bypass filters. In certain embodiments, the location of the windows may be restricted to multiple of 4 pixels, and windows are permitted to overlap. For instance, one window may be used to acquire normalization values, while another window may be used for additional statistics, such as variance, as discussed below. In one embodiment, the AF filters (e.g.,  684 ,  688 ,  690 ) may not implement pixel replication at the edge of an image frame and, therefore, in order for the AF filters to use all valid pixels, the AF windows may be set such that they are each at least 4 pixels from the top edge of the frame, at least 8 pixels from the bottom edge of the frame and at least 12 pixels from the left/right edge of the frame. In the illustrated embodiment, the following statistics may be collected and reported for each window: 
     32-bit edgeGr_sum for Gr 
     32-bit edgeR_sum for R 
     32-bit edgeB_sum for B 
     32-bit edgeGb_sum for Gb 
     32-bit edgebayerY_F 0 _sum for Y from Bayer for filter 0  (F 0 ) 
     32-bit edgebayerY_F 1 _sum for Y from Bayer for filter 1  (F 1 ) 
     32-bit edgecamY_F 0 _sum for camY for filter 0  (F 0 ) 
     32-bit edgecamY_F 1 _sum for camY for filter 1  (F 1 ) 
     In such an embodiment, the memory required for storing the AF statistics  682  may be 16 (windows) multiplied by 8 (Gr, R, B, Gb, bayerY_F 0 , bayerY_F 1 , camY_F 0 , camY_F 1 ) multiplied by 32 bits. 
     Thus, in one embodiment, the accumulated value per window may be selected between: the output of the filter (which may be configured as a default setting), the input pixel, or the input pixel squared. The selection may be made for each of the 16 AF windows, and may apply to all of the 8 AF statistics (listed above) in a given window. This may be used to normalize the AF score between two overlapping windows, one of which is configured to collect the output of the filter and one of which is configured to collect the input pixel sum. Additionally, for calculating pixel variance in the case of two overlapping windows, one window may be configured to collect the input pixel sum, and another to collect the input pixel squared sum, thus providing for a variance that may be calculated as:
 
Variance=(avg_pixel 2 )−(avg_pixel)^2
 
     Using the AF statistics, the ISP control logic  84  ( FIG. 7 ) may be configured to adjust a focal length of the lens of an image device (e.g., 30) using a series of focal length adjustments based on coarse and fine auto-focus “scores” to bring an image into focus. As discussed above, the 3×3 filters  690  for camY may provide for coarse statistics, while the horizontal filter  684  and edge detector  686  may provide for comparatively finer statistics per color component, while the 3×3 filters  688  on BayerY may provide for fine statistics on BayerY. Further, the 3×3 filters  706  on a decimated Bayer RGB signal  705  may provide coarse statistics for each color channel. As discussed further below, AF scores may be calculated based on filter output values for a particular input signal (e.g., sum of filter outputs F 0  and F 1  for camY, BayerY, Bayer RGB decimated, or based on horizontal/edge detector outputs, etc.). 
       FIG. 61  shows a graph  710  that depicts curves  712  and  714  which represent coarse and fine AF scores, respectively. As shown, the coarse AF scores based upon the coarse statistics may have a more linear response across the focal distance of the lens. Thus, at any focal position, a lens movement may generate a change in an auto focus score which may be used to detect if the image is becoming more in focus or out of focus. For instance, an increase in a coarse AF score after a lens adjustment may indicate that the focal length is being adjusted in the correct direction (e.g., towards the optical focal position). 
     However, as the optical focal position is approached, the change in the coarse AF score for smaller lens adjustments steps may decrease, making it difficult to discern the correct direction of focal adjustment. For example, as shown on graph  710 , the change in coarse AF score between coarse position (CP) CP 1  and CP 2  is represented by Δ C12 , which shows an increase in the coarse from CP 1  to CP 2 . However, as shown, from CP 3  to CP 4 , the change Δ C34  in the coarse AF score (which passes through the optimal focal position (OFP)), though still increasing, is relatively smaller. It should be understood that the positions CP 1 -CP 6  along the focal length L are not meant to necessarily correspond to the step sizes taken by the auto-focus logic along the focal length. That is, there may be additional steps taken between each coarse position that are not shown. The illustrated positions CP 1 -CP 6  are only meant to show how the change in the coarse AF score may gradually decrease as the focal position approaches the OFP. 
     Once the approximate position of the OFP is determined (e.g., based on the coarse AF scores shown in  FIG. 61 , the approximate position of the OFP may be between CP 3  and CP 5 ), fine AF score values, represented by curve  714  may be evaluated to refine the focal position. For instance, fine AF scores may be flatter when the image is out of focus, so that a large lens positional change does not cause a large change in the fine AF score. However, as the focal position approaches the optical focal position (OFP), the fine AF score may change sharply with small positional adjustments. Thus, by locating a peak or apex  715  on the fine AF score curve  714 , the OFP may be determined for the current image scene. Thus, to summarize, coarse AF scores may be used to determine the general vicinity of the optical focal position, while the fine AF scores may be used to pinpoints a more exact position within that vicinity. 
     In one embodiment, the auto-focus process may begin by acquiring coarse AF scores along the entire available focal length, beginning at position 0 and ending at position L (shown on graph  710 ) and determine the coarse AF scores at various step positions (e.g., CP 1 -CP 6 ). In one embodiment, once the focal position of the lens has reached position L, the position may reset to 0 before evaluating AF scores at various focal positions. For instance, this may be due to coil settling time of a mechanical element controlling the focal position. In this embodiment, after resetting to position 0, the focal position may be adjusted toward position L to a position that first indicated a negative change in a coarse AF score, here position CP 5  exhibiting a negative change Δ C45  with respect to position CP 4 . From position CP 5 , the focal position may be adjusted in smaller increments relative to increments used in the coarse AF score adjustments (e.g., positions FP 1 , FP 2 , FP 3 , etc.) back in the direction towards position  0 , while searching for a peak  715  in the fine AF score curve  714 . As discussed above, the focal position OFP corresponding to the peak  715  in the fine AF score curve  714  may be the optimal focal position for the current image scene. 
     As will be appreciated, the techniques described above for locating the optimal area and optimal position for focus may be referred to as “hill climbing,” in the sense that the changes in the curves for the AF scores  712  and  714  are analyzed to locate the OFP. Further, while the analysis of the coarse AF scores (curve  712 ) and the fine AF scores (curve  714 ) is shown as using same-sized steps for coarse score analysis (e.g., distance between CP 1  and CP 2 ) and same-sized steps for fine score analysis (e.g., distance between FP 1  and FP 2 ), in some embodiments, the step sizes may be varied depending on the change in the score from one position to the next. For instance, in one embodiment, the step size between CP 3  and CP 4  may be reduced relative to the step size between CP 1  and CP 2  since the overall delta in the coarse AF score (Δ C34 ) is less then the delta from CP 1  to CP 2  (Δ C12 ). 
     A method  720  depicting this process is illustrated in  FIG. 62 . Beginning at block  722 , a coarse AF score is determined for image data at various steps along the focal length, from position 0 to position L ( FIG. 61 ). Thereafter, at block  724 , the coarse AF scores are analyzed and the coarse position exhibiting the first negative change in the coarse AF score is identified as a starting point for fine AF scoring analysis. For instance, subsequently, at block  726 , the focal position is stepped back towards the initial position 0 at smaller steps, with the fine AF score at each step being analyzed until a peak in the AF score curve (e.g., curve  714  of  FIG. 61 ) is located. At block  728 , the focal position corresponding to the peak is set as the optimal focal position for the current image scene. 
     As discussed above, due to mechanical coil settling times, the embodiment of the technique shown in  FIG. 62  may be adapted to acquire coarse AF scores along the entire focal length initially, rather than analyzing each coarse position one by one and searching for an optimal focus area. Other embodiments, however, in which coil settling times are less of a concern, may analyze coarse AF scores one by one at each step, instead of searching the entire focal length. 
     In certain embodiments, the AF scores may be determined using white balanced luma values derived from Bayer RGB data. For instance, the luma value, Y, may be derived by decimating a 2×2 Bayer quad by a factor of 2, as shown in  FIG. 63 , or by decimating a 4×4 pixel block consisting of four 2×2 Bayer quads by a factor of 4, as shown in  FIG. 64 . In one embodiment, AF scores may be determined using gradients. In another embodiment, AF scores may be determined by applying a 3×3 transform using a Scharr operator, which provides rotational symmetry while minimizing weighted mean squared angular errors in the Fourier domain. By way of example, the calculation of a coarse AF score on camY using a common Scharr operator (discussed above) is shown below: 
                 AFScore   coarse     =       f   ⁡     (       [           -   3         0       3             -   10         0       10             -   3         0       3         ]     ×   in     )       +     f   (       [           -   3           -   10           -   3             0       0       0           3       10       3         ]     ×   in     )         ,         
where in represents the decimated luma Y value. In other embodiments, the AF score for both coarse and fine statistics may be calculated using other 3×3 transforms.
 
     Auto focus adjustments may also be performed differently depending on the color components, since different wavelengths of light may be affected differently by the lens, which is one reason the horizontal filter  684  is applied to each color component independently. Thus, auto-focus may still be performed even in the present of chromatic aberration in the lens. For instance, because red and blue typically focuses at a different position or distance with respect to green when chromatic aberrations are present, relative AF scores for each color may be used to determine the direction to focus. This is better illustrated in  FIG. 65 , which shows the optimal focal position for blue, red, and green color channels for a lens  740 . As shown, the optimal focal positions for red, green, and blue are depicted by reference letters R, G, and B respectively, each corresponding to an AF score, with a current focal position  742 . Generally, in such a configuration, it may be desirable to select the optimal focus position as the position corresponding to the optimal focal position for green components (e.g., since Bayer RGB has twice as many green as red or blue components), here position G. Thus, it may be expected that for an optimal focal position, the green channel should exhibit the highest auto-focus score. Thus, based on the positions of the optimal focal positions for each color (with those closer to the lens having higher AF scores), the AF logic  680  and associated control logic  84  may determine which direction to focus based on the relative AF scores for blue, green, and red. For instance, if the blue channel has a higher AF score relative to the green channel (as shown in  FIG. 65 ), then the focal position is adjusted in the negative direction (towards the image sensor) without having to first analyze in the positive direction from the current position  742 . In some embodiments, illuminant detection or analysis using color correlated temperatures (CCT) may be performed. 
     Further, as mentioned above, variance scores may also be used. For instance, pixel sums and pixel squared sum values may be accumulated for block sizes (e.g., 8×8-32×32 pixels), and may be used to derive variance scores (e.g., avg_pixel 2 )-(avg_pixel)^2). The variances may be summed to get a total variance score for each window. Smaller block sizes may be used to obtain fine variance scores, and larger block sizes may be used to obtain coarser variance scores. 
     Referring to the 3A statistics logic  468  of  FIG. 51 , the logic  468  may also be configured to collect component histograms  750  and  752 . As will be appreciated, histograms may be used to analyze the pixel level distribution in an image. This may be useful for implementing certain functions, such as histogram equalization, where the histogram data is used to determine the histogram specification (histogram matching). By way of example, luma histograms may be used for AE (e.g., for adjusting/setting sensor integration times), and color histograms may be used for AWB. In the present embodiment, histograms may be 256, 128, 64 or 32 bins (where the top 8, 7, 6, and 5 bits of the pixel is used to determine the bin, respectively) for each color component, as specified by a bin size (BinSize). For instance, when pixel data is 14-bits, an additional scale factor between 0-6 and an offset may be specified to determine what range (e.g., which 8 bits) of the pixel data is collected for statistics purposes. The bin number may be obtained as follows:
 
idx=((pixel−hist_offset)&gt;&gt;(6−hist_scale)
 
     In one embodiment, the color histogram bins are incremented only if the bin indices are in the range [0, 2^(8−BinSize)]: 
     if (idx&gt;=0 &amp;&amp; idx&lt;2^(8−BinSize))
         StatsHist[idx]+=Count;       

     In the present embodiment, the statistics processing unit  120  may include two histogram units. This first histogram  750  (Hist 0 ) may be configured to collect pixel data as part of the statistics collection after the 4×4 decimation. For Hist 0 , the components may be selected to be RGB, sRGB linear , sRGB or YC1C2 using selection circuit  756 . The second histogram  752  (Hist 1 ) may be configured to collect pixel data before the statistics pipeline (before defective pixel correction logic  460 ), as shown in more detail in  FIG. 65 . For instance, the raw Bayer RGB data (output from  124 ) may be decimated (to produce signal  754 ) using logic  760  by skipping pixels, as discussed further below. For the green channel, the color may be selected between Gr, Gb or both Gr and Gb (both Gr and Gb counts are accumulated in the Green bins). 
     In order to keep the histogram bin width the same between the two histograms, Hist 1  may be configured to collect pixel data every 4 pixels (every other Bayer quad). The start of the histogram window determines the first Bayer quad location where the histogram starts accumulating. Starting at this location, every other Bayer quad is skipped horizontally and vertically for Hist 1 . The window start location can be any pixel position for Hist 1  and, therefore pixels being skipped by the histogram calculation can be selected by changing the start window location. Hist 1  can be used to collect data, represented by  1112  in  FIG. 66 , close to the black level to assist in dynamic black level compensation at block  462 . Thus, while shown in  FIG. 66  as being separate from the 3A statistics logic  468  for illustrative purposes, it should be understood that the histogram  752  may actually be part of the statistics written to memory, and may be actually be physically located within the statistics processing unit  120 . 
     In the present embodiment, the red (R) and blue (B) bins may be 20-bits, with the green (G) bin is 21-bits (Green is larger to accommodate the Gr and Gb accumulation in Hist 1 ). This allows for a maximum picture size of 4160 by 3120 pixels (12 MP). The internal memory size required is 3×256×20(1) bits (3 color components, 256 bins). 
     With regard to memory format, statistics for AWB/AE windows, AF windows, 2D color histogram, and component histograms may be mapped to registers to allow early access by firmware. In one embodiment, two memory pointers may be used to write statistics to memory, one for tile statistics  674 , and one for luma row sums  678 , followed by all other collected statistics. All statistics are written to external memory, which may be DMA memory. The memory address registers may be double-buffered so that a new location in memory can be specified on every frame. 
     Before proceeding with a detailed discussion of the ISP pipe logic  82  downstream from the ISP front-end logic  80 , it should understood that the arrangement of various functional logic blocks in the statistics processing units  120  and  122  (e.g., logic blocks  460 ,  462 ,  464 ,  466 , and  468 ) and the ISP front-end pixel processing unit  130  (e.g., logic blocks  298  and  300 ) are intended to illustrate only one embodiment of the present technique. Indeed, in other embodiments, the logic blocks illustrated herein may be arranged in different ordering, or may include additional logic blocks that may perform additional image processing functions not specifically described herein. Further, it should be understood that the image processing operations performed in the statistics processing units (e.g.,  120  and  122 ), such as lens shading correction, defective pixel detection/correction, and black level compensation, are performed within the statistics processing units for the purposes of collecting statistical data. Thus, processing operations performed upon the image data received by the statistical processing units are not actually reflected in the image signal  109  (FEProcOut) that is output from the ISP front-end pixel processing logic  130  and forwarded to the ISP pipe processing logic  82 . 
     Before continuing, it should also be noted, that given sufficient processing time and the similarity between many of the processing requirements of the various operations described herein, it is possible to reconfigure the functional blocks shown herein to perform image processing in a sequential manner, rather than a pipe-lined nature. As will be understood, this may further reduce the overall hardware implementation costs, but may also increase bandwidth to external memory (e.g., to cache/store intermediate results/data). 
     The ISP Pipeline (“Pipe”) Processing Logic 
     Having described the ISP front-end logic  80  in detail above, the present discussion will now shift focus to the ISP pipe processing logic  82 . Generally, the function of the ISP pipe logic  82  is to receive raw image data, which may be provided from the ISP front-end logic  80  or retrieved from memory  108 , and to perform additional image processing operations, i.e., prior to outputting the image data to the display device  28 . 
     A block diagram showing an embodiment of the ISP pipe logic  82  is depicted in  FIG. 67 . As illustrated, the ISP pipe logic  82  may include raw processing logic  900 , RGB processing logic  902 , and YCbCr processing logic  904 . The raw processing logic  900  may perform various image processing operations, such as defective pixel detection and correction, lens shading correction, demosaicing, as well as applying gains for auto-white balance and/or setting a black level, as will be discussed further below. As shown in the present embodiment, the input signal  908  to the raw processing logic  900  may be the raw pixel output  109  (signal FEProcOut) from the ISP front-end logic  80  or the raw pixel data  112  from the memory  108 , depending on the present configuration of the selection logic  906 . 
     As a result of demosaicing operations performed within the raw processing logic  900 , the image signal output  910  may be in the RGB domain, and may be subsequently forwarded to the RGB processing logic  902 . For instance, as shown in  FIG. 67 , the RGB processing logic  902  receives the signal  916 , which may be the output signal  910  or an RGB image signal  912  from the memory  108 , depending on the present configuration of the selection logic  914 . The RGB processing logic  902  may provide for various RGB color adjustment operations, including color correction (e.g., using a color correction matrix), the application of color gains for auto-white balancing, as well as global tone mapping, as will be discussed further below. The RGB processing logic  904  may also provide for the color space conversion of RGB image data to the YCbCr (luma/chroma) color space. Thus, the image signal output  918  may be in the YCbCr domain, and may be subsequently forwarded to the YCbCr processing logic  904 . 
     For instance, as shown in  FIG. 67 , the YCbCr processing logic  904  receives the signal  924 , which may be the output signal  918  from the RGB processing logic  902  or a YCbCr signal  920  from the memory  108 , depending on the present configuration of the selection logic  922 . As will be discussed in further detail below, the YCbCr processing logic  904  may provide for image processing operations in the YCbCr color space, including scaling, chroma suppression, luma sharpening, brightness, contrast, and color (BCC) adjustments, YCbCr gamma mapping, chroma decimation, and so forth. The image signal output  926  of the YCbCr processing logic  904  may be sent to the memory  108 , or may be output from the ISP pipe processing logic  82  as the image signal  114  ( FIG. 7 ). The image signal  114  may be sent to the display device  28  (either directly or via memory  108 ) for viewing by the user, or may be further processed using a compression engine (e.g., encoder  118 ), a CPU/GPU, a graphics engine, or the like. 
     In accordance with embodiments of the present techniques, the ISP pipe logic  82  may support the processing of raw pixel data in 8-bit, 10-bit, 12-bit, or 14-bit formats. For instance, in one embodiment, 8-bit, 10-bit, or 12-bit input data may be converted to 14-bit at the input of the raw processing logic  900 , and raw processing and RGB processing operations may be performed with 14-bit precision. In the latter embodiment, the 14-bit image data may be down-sampled to 10 bits prior to the conversion of the RGB data to the YCbCr color space, and the YCbCr processing (logic  904 ) may be performed with 10-bit precision. 
     In order to provide a comprehensive description of the various functions provided by the ISP pipe processing logic  82 , each of the raw processing logic  900 , RGB processing logic  902 , and YCbCr processing logic  904 , as well as internal logic for performing various image processing operations that may be implemented in each respective unit of logic  900 ,  902 , and  904 , will be discussed sequentially below, beginning with the raw processing logic  900 . For instance, referring now to  FIG. 68 , a block diagram showing a more detailed view of an embodiment of the raw processing logic  900  is illustrated, in accordance with an embodiment of the present technique. As shown, the raw processing logic  900  includes the gain, offset, and clamping (GOC) logic  930 , defective pixel detection/correction (DPDC) logic  932 , the noise reduction logic  934 , lens shading correction logic  936 , GOC logic  938 , and demosaicing logic  940 . Further, while the examples discussed below assume the use of a Bayer color filter array with the image sensor(s)  90 , it should be understood that other embodiments of the present technique may utilize different types of color filters as well. 
     The input signal  908 , which may be a raw image signal, is first received by the gain, offset, and clamping (GOC) logic  930 . The GOC logic  930  may provide similar functions and may be implemented in a similar manner with respect to the BLC logic  462  of the statistics processing unit  120  of the ISP front-end logic  80 , as discussed above in  FIG. 37 . For instance, the GOC logic  930  may provide digital gain, offsets and clamping (clipping) independently for each color component R, B, Gr, and Gb of a Bayer image sensor. Particularly, the GOC logic  930  may perform auto-white balance or set the black level of the raw image data. Further, in some embodiments, the GOC logic  930  may also be used correct or compensate for an offset between the Gr and Gb color components. 
     In operation, the input value for the current pixel is first offset by a signed value and multiplied by a gain. This operation may be performed using the formula shown in Equation 11 above, wherein X represents the input pixel value for a given color component R, B, Gr, or Gb, O[c] represents a signed 16-bit offset for the current color component c, and G[c] represents a gain value for the color component c. The values for G[c] may be previously determined during statistics processing (e.g., in the ISP front-end block  80 ). In one embodiment, the gain G[c] may be a 16-bit unsigned number with 2 integer bits and 14 fraction bits (e.g., 2.14 floating point representation), and the gain G[c] may be applied with rounding. By way of example only, the gain G[c] may have a range of between 0 to 4X. 
     The computed pixel value Y (which includes the gain G[c] and offset O[c]) from Equation 11 is then be clipped to a minimum and a maximum range in accordance with Equation 12. As discussed above, the variables min[c] and max[c] may represent signed 16-bit “clipping values” for the minimum and maximum output values, respectively. In one embodiment, the GOC logic  930  may also be configured to maintain a count of the number of pixels that were clipped above and below maximum and minimum ranges, respectively, for each color component. 
     Subsequently, the output of the GOC logic  930  is forwarded to the defective pixel detection and correction logic  932 . As discussed above with reference to  FIG. 37  (DPDC logic  460 ), defective pixels may attributable to a number of factors, and may include “hot” (or leaky) pixels, “stuck” pixels, and “dead pixels, wherein hot pixels exhibit a higher than normal charge leakage relative to non-defective pixels, and thus may appear brighter than non-defective pixel, and wherein a stuck pixel appears as always being on (e.g., fully charged) and thus appears brighter, whereas a dead pixel appears as always being off As such, it may be desirable to have a pixel detection scheme that is robust enough to identify and address different types of failure scenarios. Particularly, when compared to the front-end DPDC logic  460 , which may provide only dynamic defect detection/correction, the pipe DPDC logic  932  may provide for fixed or static defect detection/correction, dynamic defect detection/correction, as well as speckle removal. 
     In accordance with embodiments of the presently disclosed techniques, defective pixel correction/detection performed by the DPDC logic  932  may occur independently for each color component (e.g., R, B, Gr, and Gb), and may include various operations for detecting defective pixels, as well as for correcting the detected defective pixels. For instance, in one embodiment, the defective pixel detection operations may provide for the detection of static defects, dynamics defects, as well as the detection of speckle, which may refer to the electrical interferences or noise (e.g., photon noise) that may be present in the imaging sensor. By analogy, speckle may appear on an image as seemingly random noise artifacts, similar to the manner in which static may appear on a display, such as a television display. Further, as noted above, dynamic defection correction is regarded as being dynamic in the sense that the characterization of a pixel as being defective at a given time may depend on the image data in the neighboring pixels. For example, a stuck pixel that is always on maximum brightness may not be regarded as a defective pixel if the location of the stuck pixel is in an area of the current image that is dominate by bright white colors. Conversely, if the stuck pixel is in a region of the current image that is dominated by black or darker colors, then the stuck pixel may be identified as a defective pixel during processing by the DPDC logic  932  and corrected accordingly. 
     With regard to static defect detection, the location of each pixel is compared to a static defect table, which may store data corresponding to the location of pixels that are known to be defective. For instance, in one embodiment, the DPDC logic  932  may monitor the detection of defective pixels (e.g., using a counter mechanism or register) and, if a particular pixel is observed as repeatedly failing, the location of that pixel is stored into the static defect table. Thus, during static defect detection, if it is determined that the location of the current pixel is in the static defect table, then the current pixel is identified as being a defective pixel, and a replacement value is determined and temporarily stored. In one embodiment, the replacement value may be the value of the previous pixel (based on scan order) of the same color component. The replacement value may be used to correct the static defect during dynamic/speckle defect detection and correction, as will be discussed below. Additionally, if the previous pixel is outside of the raw frame  278  ( FIG. 19 ), then its value is not used, and the static defect may be corrected during the dynamic defect correction process. Further, due to memory considerations, the static defect table may store a finite number of location entries. For instance, in one embodiment, the static defect table may be implemented as a FIFO queue configured to store a total of 16 locations for every two lines of image data. The locations in defined in the static defect table will, nonetheless, be corrected using a previous pixel replacement value (rather than via the dynamic defect detection process discussed below). As mentioned above, embodiments of the present technique may also provide for updating the static defect table intermittently over time. 
     Embodiments may provide for the static defect table to be implemented in on-chip memory or off-chip memory. As will be appreciated, using an on-chip implementation may increase overall chip area/size, while using an off-chip implementation may reduce chip area/size, but increase memory bandwidth requirements. Thus, it should be understood that the static defect table may be implemented either on-chip or off-chip depending on specific implementation requirements, i.e., the total number of pixels that are to be stored within the static defect table. 
     The dynamic defect and speckle detection processes may be time-shifted with respect to the static defect detection process discussed above. For instance, in one embodiment, the dynamic defect and speckle detection process may begin after the static defect detection process has analyzed two scan lines (e.g., rows) of pixels. As can be appreciated, this allows for the identification of static defects and their respective replacement values to be determined before dynamic/speckle detection occurs. For example, during the dynamic/speckle detection process, if the current pixel was previously marked as being a static defect, rather than applying dynamic/speckle detection operations, the static defect is simply corrected using the previously assessed replacement value. 
     With regard to dynamic defect and speckle detection, these processes may occur sequentially or in parallel. The dynamic defect and speckle detection and correction that is performed by the DPDC logic  932  may rely on adaptive edge detection using pixel-to-pixel direction gradients. In one embodiment, the DPDC logic  932  may select the eight immediate neighbors of the current pixel having the same color component that are within the raw frame  278  ( FIG. 19 ) are used. In other words, the current pixels and its eight immediate neighbors P 0 , P 1 , P 2 , P 3 , P 4 , P 5 , P 6 , and P 7  may form a 3×3 area, as shown below in  FIG. 69 . 
     It should be noted, however, that depending on the location of the current pixel P, pixels outside the raw frame  278  are not considered when calculating pixel-to-pixel gradients. For example, with regard to the “top-left” case  942  shown in  FIG. 69 , the current pixel P is at the top-left corner of the raw frame  278  and, thus, the neighboring pixels P 0 , P 1 , P 2 , P 3 , and P 5  outside of the raw frame  278  are not considered, leaving only the pixels P 4 , P 6 , and P 7  (N=3). In the “top” case  944 , the current pixel P is at the top-most edge of the raw frame  278  and, thus, the neighboring pixels P 0 , P 1 , and P 2  outside of the raw frame  278  are not considered, leaving only the pixels P 3 , P 4 , P 5 , P 6 , and P 7  (N=5). Next, in the “top-right” case  946 , the current pixel P is at the top-right corner of the raw frame  278  and, thus, the neighboring pixels P 0 , P 1 , P 2 , P 4 , and P 7  outside of the raw frame  278  are not considered, leaving only the pixels P 3 , P 5 , and P 6  (N=3). In the “left” case  948 , the current pixel P is at the left-most edge of the raw frame  278  and, thus, the neighboring pixels P 0 , P 3 , and P 5  outside of the raw frame  278  are not considered, leaving only the pixels P 1 , P 2 , P 4 , P 6 , and P 7  (N=5). 
     In the “center” case  950 , all pixels P 0 -P 7  lie within the raw frame  278  and are thus used in determining the pixel-to-pixel gradients (N=8). In the “right” case  952 , the current pixel P is at the right-most edge of the raw frame  278  and, thus, the neighboring pixels P 2 , P 4 , and P 7  outside of the raw frame  278  are not considered, leaving only the pixels P 0 , P 1 , P 3 , P 5 , and P 6  (N=5). Additionally, in the “bottom-left” case  954 , the current pixel P is at the bottom-left corner of the raw frame  278  and, thus, the neighboring pixels P 0 , P 3 , P 5 , P 6 , and P 7  outside of the raw frame  278  are not considered, leaving only the pixels P 1 , P 2 , and P 4  (N=3). In the “bottom” case  956 , the current pixel P is at the bottom-most edge of the raw frame  278  and, thus, the neighboring pixels P 5 , P 6 , and P 7  outside of the raw frame  278  are not considered, leaving only the pixels P 0 , P 1 , P 2 , P 3 , and P 4  (N=5). Finally, in the “bottom-right” case  958 , the current pixel P is at the bottom-right corner of the raw frame  278  and, thus, the neighboring pixels P 2 , P 4 , P 5 , P 6 , and P 7  outside of the raw frame  278  are not considered, leaving only the pixels P 0 , P 1 , and P 3  (N=3). 
     Thus, depending upon the position of the current pixel P, the number of pixels used in determining the pixel-to-pixel gradients may be 3, 5, or 8. In the illustrated embodiment, for each neighboring pixel (k=0 to 7) within the picture boundary (e.g., raw frame  278 ), the pixel-to-pixel gradients may be calculated as follows:
 
 G   k =abs( P−P   k ), for 0≦ k≦ 7 (only for  k  within the raw frame)  (51)
 
Additionally, an average gradient, G av , may be calculated as the difference between the current pixel and the average, P av , of its surrounding pixels, as shown by the equations below:
 
                         P   av     =       (       ∑   k   N     ⁢     P   k       )     N       ,       wherein   ⁢           ⁢   N     =   3     ,   5   ,           ⁢   or     ⁢           ⁢     
     ⁢     8   ⁢     (     depending   ⁢           ⁢   on   ⁢           ⁢   pixel   ⁢           ⁢   position     )               (     52   ⁢   a     )                 G   av     =     abs   ⁡     (     P   -     P   av       )               (     52   ⁢   b     )               
The pixel-to-pixel gradient values (Equation 51) may be used in determining a dynamic defect case, and the average of the neighboring pixels (Equations 52a and 52b) may be used in identifying speckle cases, as discussed further below.
 
     In one embodiment, dynamic defect detection may be performed by the DPDC logic  932  as follows. First, it is assumed that a pixel is defective if a certain number of the gradients G k  are at or below a particular threshold, denoted by the variable dynTh (dynamic defect threshold). Thus, for each pixel, a count (C) of the number of gradients for neighboring pixels inside the picture boundaries that are at or below the threshold dynTh is accumulated. The threshold dynTh may be a combination of a fixed threshold component and a dynamic threshold component that may depend on the “activity” present the surrounding pixels. For instance, in one embodiment, the dynamic threshold component for dynTh may be determined by calculating a high frequency component value P hf  based upon summing the absolute difference between the average pixel values P av  (Equation 52a) and each neighboring pixel, as illustrated below: 
                       P   hf     =         8   N     ⁢       ∑   k   N     ⁢       abs   ⁡     (       P   av     -     P   k       )       ⁢             ⁢             ⁢   wherein   ⁢           ⁢   N         =   3       ,   5   ,     or   ⁢           ⁢   8             (     52   ⁢   c     )               
In instances where the pixel is located at an image corner (N=3) or at an image edge (N=5), the P hf  may be multiplied by the 8/3 or 8/5, respectively. As can be appreciated, this ensures that the high frequency component P hf  is normalized based on eight neighboring pixels (N=8).
 
     Once P hf  is determined, the dynamic defect detection threshold dynTh may be computed as shown below:
 
dyn Th =dyn Th   1 +(dyn Th   2   ×P   hf ),  (53)
 
wherein dynTh 1  represents the fixed threshold component, and wherein dynTh 2  represents the dynamic threshold component, and is a multiplier for P hf  in Equation 53. A different fixed threshold component dynTh 1  may be provided for each color component, but for each pixel of the same color, dynTh 1  is the same. By way of example only, dynTh 1  may be set so that it is at least above the variance of noise in the image.
 
     The dynamic threshold component dynTh 2  may be determined based on some characteristic of the image. For instance, in one embodiment, dynTh 2  may be determined using stored empirical data regarding exposure and/or sensor integration time. The empirical data may be determined during calibration of the image sensor (e.g., 90), and may associate dynamic threshold component values that may be selected for dynTh 2  with each of a number of data points. Thus, based upon the current exposure and/or sensor integration time value, which may be determined during statistics processing in the ISP front-end logic  80 , dynTh 2  may be determined by selecting the dynamic threshold component value from the stored empirical data that corresponds to the current exposure and/or sensor integration time value. Additionally, if the current exposure and/or sensor integration time value does not correspond directly to one of the empirical data points, then dynTh 2  may be determined by interpolating the dynamic threshold component values associated with the data points between which the current exposure and/or sensor integration time value falls. Further, like the fixed threshold component dynTh 1 , the dynamic threshold component dynTh 2  may have different values for each color component. Thus, composite threshold value dynTh may vary for each color component (e.g., R, B, Gr, Gb). 
     As mentioned above, for each pixel, a count C of the number of gradients for neighboring pixels inside the picture boundaries that are at or below the threshold dynTh is determined. For instance, for each neighboring pixel within the raw frame  278 , the accumulated count C of the gradients G k  that are at or below the threshold dynTh may be computed as follows: 
                     C   =       ∑   k   N     ⁢     (       G   k     ≤     dyn   ⁢           ⁢   Th       )         ,     
     ⁢       for   ⁢           ⁢   0     ≤   k   ≤     7   ⁢     (     only   ⁢           ⁢   for   ⁢           ⁢   k   ⁢           ⁢   within   ⁢           ⁢   the   ⁢             ⁢             ⁢   raw   ⁢           ⁢   frame     )                 (   54   )               
Next, if the accumulated count C is determined to be less than or equal to a maximum count, denoted by the variable dynMaxC, then the pixel may be considered as a dynamic defect. In one embodiment, different values for dynMaxC may be provided for N=3 (corner), N=5 (edge), and N=8 conditions. This logic is expressed below:
 
if ( C ≦dynMax C ), then the current pixel  P  is defective.  (55)
 
     As mentioned above, the location of defective pixels may be stored into the static defect table. In some embodiments, the minimum gradient value (min(G k )) calculated during dynamic defect detection for the current pixel may be stored and may be used to sort the defective pixels, such that a greater minimum gradient value indicates a greater “severity” of a defect and should be corrected during pixel correction before less severe defects are corrected. In one embodiment, a pixel may need to be processed over multiple imaging frames before being stored into the static defect table, such as by filtering the locations of defective pixels over time. In the latter embodiment, the location of the defective pixel may be stored into the static defect table only if the defect appears in a particular number of consecutive images at the same location. Further, in some embodiments, the static defect table may be configured to sort the stored defective pixel locations based upon the minimum gradient values. For instance, the highest minimum gradient value may indicate a defect of greater “severity.” By ordering the locations in this manner, the priority of static defect correction may be set, such that the most severe or important defects are corrected first. Additionally, the static defect table may be updated over time to include newly detected static defects, and ordering them accordingly based on their respective minimum gradient values. 
     Speckle detection, which may occur in parallel with the dynamic defect detection process described above, may be performed by determining if the value G av  (Equation 52b) is above a speckle detection threshold spkTh. Like the dynamic defect threshold dynTh, the speckle threshold spkTh may also include fixed and dynamic components, referred to by spkTh 1  and spkTh 2 , respectively. In general, the fixed and dynamic components spkTh 1  and spkTh 2  may be set more “aggressively” compared to the dynTh 1  and dynTh 2  values, in order to avoid falsely detecting speckle in areas of the image that may be more heavily textured and others, such as text, foliage, certain fabric patterns, etc. Accordingly, in one embodiment, the dynamic speckle threshold component spkTh 2  may be increased for high-texture areas of the image, and decreased for “flatter” or more uniform areas. The speckle detection threshold spkTh may be computed as shown below:
 
spk Th =spk Th   1 +(spk Th   2   ×P   hf ),  (56)
 
wherein spkTh 1  represents the fixed threshold component, and wherein spkTh 2  represents the dynamic threshold component. The detection of speckle may then be determined in accordance with the following expression:
 
if ( G   av &gt;spk Th ), then the current pixel  P  is speckled.  (57)
 
     Once defective pixels have been identified, the DPDC logic  932  may apply pixel correction operations depending on the type of defect detected. For instance, if the defective pixel was identified as a static defect, the pixel is replaced with the stored replacement value, as discussed above (e.g., the value of the previous pixel of the same color component). If the pixel was identified as either a dynamic defect or as speckle, then pixel correction may be performed as follows. First, gradients are computed as the sum of the absolute difference between the center pixel and a first and second neighbor pixels (e.g., computation of G k  of Equation 51) for four directions, a horizontal (h) direction, a vertical (v) direction, a diagonal-positive direction (dp), and a diagonal-negative direction (dn), as shown below:
 
 G   h   =G   3   +G   4   (58)
 
 G   v   =G   1   +G   6   (59)
 
 G   dp   =G   2   +G   5   (60)
 
 G   dn   =G   0   +G   7   (61)
 
     Next, the corrective pixel value P C  may be determined via linear interpolation of the two neighboring pixels associated with the directional gradient G h , G v , G dp , and G dn  that has the smallest value. For instance, in one embodiment, the logic statement below may express the calculation of P C : 
                           if   ⁡     (     min   ==     G   h       )       ⁢           ⁢     P   C       =         P   3     +     P   4       2       ;     ⁢     
     ⁢         else   ⁢           ⁢     if   ⁡     (     min   ==     G   v       )       ⁢           ⁢     P   C       =         P   1     +     P   6       2       ;     ⁢     
     ⁢         else   ⁢           ⁢     if   ⁡     (     min   ==     G   dp       )       ⁢           ⁢     P   C       =         P   2     +     P   5       2       ;     ⁢     
     ⁢         else   ⁢           ⁢     if   ⁡     (     min   ==     G   dn       )       ⁢           ⁢     P   C       =         P   0     +     P   7       2       ;             (   62   )               
The pixel correction techniques implemented by the DPDC logic  932  may also provide for exceptions at boundary conditions. For instance, if one of the two neighboring pixels associated with the selected interpolation direction is outside of the raw frame, then the value of the neighbor pixel that is within the raw frame is substituted instead. Thus, using this technique, the corrective pixel value will be equivalent to the value of the neighbor pixel within the raw frame.
 
     It should be noted that the defective pixel detection/correction techniques applied by the DPDC logic  932  during the ISP pipe processing is more robust compared to the DPDC logic  460  in the ISP front-end logic  80 . As discussed in the embodiment above, the DPDC logic  460  performs only dynamic defect detection and correction using neighboring pixels in only the horizontal direction, whereas the DPDC logic  932  provides for the detection and correction of static defects, dynamic defects, as well as speckle, using neighboring pixels in both horizontal and vertical directions. 
     As will be appreciated, the storage of the location of the defective pixels using a static defect table may provide for temporal filtering of defective pixels with lower memory requirements. For instance, compared to many conventional techniques which store entire images and apply temporal filtering to identify static defects over time, embodiments of the present technique only store the locations of defective pixels, which may typically be done using only a fraction of the memory required to store an entire image frame. Further, as discussed above, the storing of a minimum gradient value (min(G k )), allows for an efficient use of the static defect table prioritizing the order of the locations at which defective pixels are corrected (e.g., beginning with those that will be most visible). 
     Additionally, the use of thresholds that include a dynamic component (e.g., dynTh 2  and spkTh 2 ) may help to reduce false defect detections, a problem often encountered in conventional image processing systems when processing high texture areas of an image (e.g., text, foliage, certain fabric patterns, etc.). Further, the use of directional gradients (e.g., h, v, dp, dn) for pixel correction may reduce the appearance of visual artifacts if a false defect detection occurs. For instance, filtering in the minimum gradient direction may result in a correction that still yields acceptable results under most cases, even in cases of false detection. Additionally, the inclusion of the current pixel P in the gradient calculation may improve the accuracy of the gradient detection, particularly in the case of hot pixels. 
     The above-discussed defective pixel detection and correction techniques implemented by the DPDC logic  932  may be summarized by a series of flow charts provided in  FIGS. 70-72 . For instance, referring first to  FIG. 70 , a process  960  for detecting static defects is illustrated. Beginning initially at step  962 , an input pixel P is received at a first time, T 0 . Next, at step  964 , the location of the pixel P is compared to the values stored in a static defect table. Decision logic  966  determines whether the location of the pixel P is found in the static defect table. If the location of P is in the static defect table, then the process  960  continues to step  968 , wherein the pixel P is marked as a static defect and a replacement value is determined. As discussed above, the replacement value may be determined based upon the value of the previous pixel (in scan order) of the same color component. The process  960  then continues to step  970 , at which the process  960  proceeds to the dynamic and speckle detection process  980 , illustrated in  FIG. 71 . Additionally, if at decision logic  966 , the location of the pixel P is determined not to be in the static defect table, then the process  960  proceeds to step  970  without performing step  968 . 
     Continuing to  FIG. 71 , the input pixel P is received at time T 1 , as shown by step  982 , for processing to determine whether a dynamic defect or speckle is present. Time T 1  may represent a time-shift with respect to the static defect detection process  960  of  FIG. 70 . As discussed above, the dynamic defect and speckle detection process may begin after the static defect detection process has analyzed two scan lines (e.g., rows) of pixels, thus allowing time for the identification of static defects and their respective replacement values to be determined before dynamic/speckle detection occurs. 
     The decision logic  984  determines if the input pixel P was previously marked as a static defect (e.g., by step  968  of process  960 ). If P is marked as a static defect, then the process  980  may continue to the pixel correction process shown in  FIG. 72  and may bypass the rest of the steps shown in  FIG. 71 . If the decision logic  984  determines that the input pixel P is not a static defect, then the process continues to step  986 , and neighboring pixels are identified that may be used in the dynamic defect and speckle process. For instance, in accordance with the embodiment discussed above and illustrated in  FIG. 69 , the neighboring pixels may include the immediate  8  neighbors of the pixel P (e.g., P 0 -P 7 ), thus forming a 3×3 pixel area. Next, at step  988 , pixel-to-pixel gradients are calculated with respect to each neighboring pixel within the raw frame  278 , as described in Equation 51 above. Additionally, an average gradient (G av ) may be calculated as the difference between the current pixel and the average of its surrounding pixels, as shown in Equations 52a and 52b. 
     The process  980  then branches to step  990  for dynamic defect detection and to decision logic  998  for speckle detection. As noted above, dynamic defect detection and speckle detection may, in some embodiments, occur in parallel. At step  990 , a count C of the number of gradients that are less than or equal to the threshold dynTh is determined. As described above, the threshold dynTh may include fixed and dynamic components and, in one embodiment, may be determined in accordance with Equation 53 above. If C is less than or equal to a maximum count, dynMaxC, then the process  980  continues to step  996 , and the current pixel is marked as being a dynamic defect. Thereafter, the process  980  may continue to the pixel correction process shown in  FIG. 72 , which will be discussed below. 
     Returning back the branch after step  988 , for speckle detection, the decision logic  998  determines whether the average gradient G av  is greater than a speckle detection threshold spkTh, which may also include a fixed and dynamic component. If G av  is greater than the threshold spkTh, then the pixel P is marked as containing speckle at step  1000  and, thereafter, the process  980  continues to  FIG. 72  for the correction of the speckled pixel. Further, if the output of both of the decision logic blocks  992  and  998  are “NO,” then this indicates that the pixel P does not contain dynamic defects, speckle, or even static defects (decision logic  984 ). Thus, when the outputs of decision logic  992  and  998  are both “NO,” the process  980  may conclude at step  994 , whereby the pixel P is passed unchanged, as no defects (e.g., static, dynamic, or speckle) were detected. 
     Continuing to  FIG. 72 , a pixel correction process  1010  in accordance with the techniques described above is provided. At step  1012 , the input pixel P is received from process  980  of  FIG. 71 . It should be noted that the pixel P may be received by process  1010  from step  984  (static defect) or from steps  996  (dynamic defect) and  1000  (speckle defect). The decision logic  1014  then determines whether the pixel P is marked as a static defect. If the pixel P is a static defect, then the process  1010  continues and ends at step  1016 , whereby the static defect is corrected using the replacement value determined at step  968  ( FIG. 70 ). 
     If the pixel P is not identified as a static defect, then the process  1010  continues from decision logic  1014  to step  1018 , and directional gradients are calculated. For instance, as discussed above with reference to Equations 58-61, the gradients may be computed as the sum of the absolute difference between the center pixel and first and second neighboring pixels for four directions (h, v, dp, and dn). Next, at step  1020 , the directional gradient having the smallest value is identified and, thereafter, decision logic  1022  assesses whether one of the two neighboring pixels associated with the minimum gradient is located outside of the image frame (e.g., raw frame  278 ). If both neighboring pixels are within the image frame, then the process  1010  continues to step  1024 , and a pixel correction value (P C ) is determined by applying linear interpolation to the values of the two neighboring pixels, as illustrated by Equation 62. Thereafter, the input pixel P may be corrected using the interpolated pixel correction value P C , as shown at step  1030 . 
     Returning to the decision logic  1022 , if it is determined that one of the two neighboring pixels are located outside of the image frame (e.g., raw frame  165 ), then instead of using the value of the outside pixel (Pout), the DPDC logic  932  may substitute the value of Pout with the value of the other neighboring pixel that is inside the image frame (Pin), as shown at step  1026 . Thereafter, at step  1028 , the pixel correction value P C  is determined by interpolating the values of Pin and the substituted value of Pout. In other words, in this case, P C  may be equivalent to the value of Pin. Concluding at step  1030 , the pixel P is corrected using the value P C . Before continuing, it should be understood that the particular defective pixel detection and correction processes discussed herein with reference to the DPDC logic  932  are intended to reflect only one possible embodiment of the present technique. Indeed, depending on design and/or cost constraints, a number of variations are possible, and features may be added or removed such that the overall complexity and robustness of the defect detection/correction logic is between the simpler detection/correction logic  460  implemented in the ISP front-end block  80  and the defect detection/correction logic discussed here with reference to the DPDC logic  932 . 
     Referring back to  FIG. 68 , the corrected pixel data is output from the DPDC logic  932  and then received by the noise reduction logic  934  for further processing. In one embodiment, the noise reduction logic  934  may be configured to implements two-dimensional edge-adaptive low pass filtering to reduce noise in the image data while maintaining details and textures. The edge-adaptive thresholds may be set (e.g., by the control logic  84 ) based upon the present lighting levels, such that filtering may be strengthened under low light conditions. Further, as briefly mentioned above with regard to the determination of the dynTh and spkTh values, noise variance may be determined ahead of time for a given sensor so that the noise reduction thresholds can be set just above noise variance, such that during the noise reduction processing, noise is reduced without significantly affecting textures and details of the scene (e.g., avoid/reduce false detections). Assuming a Bayer color filter implementation, the noise reduction logic  934  may process each color component Gr, R, B, and Gb independently using a separable 7-tap horizontal filter and a 5-tap vertical filter. In one embodiment, the noise reduction process may be carried out by correcting for non-uniformity on the green color components (Gb and Gr), and then performing horizontal filtering and vertical filtering. 
     Green non-uniformity (GNU) is generally characterized by a slight brightness difference between the Gr and Gb pixels given a uniformly illuminated flat surface. Without correcting or compensating for this non-uniformity, certain artifacts, such as a “maze” artifact, may appear in the full color image after demosaicing. During the green non-uniformity process may include determining, for each green pixel in the raw Bayer image data, if the absolute difference between a current green pixel (G 1 ) and the green pixel to the right and below (G 2 ) the current pixel is less than a GNU correction threshold (gnuTh).  FIG. 73  illustrates the location of the G 1  and G 2  pixels in a 2×2 area of the Bayer pattern. As shown, the color of the pixels bordering G 1  may be depending upon whether the current green pixel is a Gb or Gr pixel. For instance, if G 1  is Gr, then G 2  is Gb, the pixel to the right of G 1  is R (red), and the pixel below G 1  is B (blue). Alternatively, if G 1  is Gb, then G 2  is Gr, and the pixel to the right of G 1  is B, whereas the pixel below G 1  is R. If the absolute difference between G 1  and G 2  is less than the GNU correction threshold value, then current green pixel G 1  is replaced by the average of G 1  and G 2 , as shown by the logic below: 
                     if   ⁡     (       abs   ⁡     (       G   ⁢           ⁢   1     -     G   ⁢           ⁢   2       )       ≤   gnuTh     )       ;       G   ⁢           ⁢   1     =         G   ⁢           ⁢   1     +     G   ⁢           ⁢   2       2               (   63   )               
As can be appreciated, the application of green non-uniformity correction in this manner may help to prevent the G 1  and G 2  pixels from being averaged across edges, thus improving and/or preserving sharpness.
 
     Horizontal filtering is applied subsequent to green non-uniformity correction and may, in one embodiment, provide a 7-tap horizontal filter. Gradients across the edge of each filter tap are computed, and if it is above a horizontal edge threshold (horzTh), the filter tap is folded to the center pixel, as will be illustrated below. The horizontal filter may process the image data independently for each color component (R, B, Gr, Gb) and may use unfiltered values as inputs values. 
     By way of example,  FIG. 74  shows a graphical depiction of a set of horizontal pixels P 0  to P 6 , with a center tap positioned at P 3 . Based upon the pixels shown in  FIG. 74 , edge gradients for each filter tap may be calculated as follows:
 
 Eh 0=abs( P 0− P 1)  (64)
 
 Eh 1=abs( P 1− P 2)  (65)
 
 Eh 2=abs( P 2− P 3)  (66)
 
 Eh 3=abs( P 3− P 4)  (67)
 
 Eh 4=abs( P 4− P 5)  (68)
 
 Eh 5=abs( P 5− P 6)  (69)
 
The edge gradients Eh 0 -Eh 5  may then be utilized by the horizontal filter component to determine a horizontal filtering output, P horz , using the formula shown in Equation 70 below:
 
 P   horz   =C 0×[( Eh 2&gt;horz Th[c ])? P 3:( Eh 1&gt;horz Th[c ])? P 2:( Eh 0&gt;horz Th[c ])? P 1: P 0]+ C 1×[( Eh 2&gt;horz Th[c ])? P 3:( Eh 1&gt;horz Th[c ])? P 2: P 1]+ C 2×[( Eh 2&gt;horz Th[c ])? P 3: P 2]+ C 3× P 3+ C 4×[( Eh 3&gt;horz Th[c ])? P 3: P 4]+ C 5×[( Eh 3&gt;horz Th[c ])? P 3:( Eh 4&gt;horz Th[c ])? P 4: P 5]+ C 6×[( Eh 3&gt;horz Th[c ])? P 3:( Eh 4&gt;horz Th[c ])? P 4:( Eh 5&gt;horz Th[c ])? P 5: P 6],  (70)
 
wherein horzTh[c] is the horizontal edge threshold for each color component c (e.g., R, B, Gr, and Gb), and wherein C 0 -C 6  are the filter tap coefficients corresponding to pixels P 0 -P 6 , respectively. The horizontal filter output P horz  may be applied at the center pixel P 3  location. In one embodiment, the filter tap coefficients C 0 -C 6  may be 16-bit two&#39;s complement values with 3 integer bits and 13 fractional bits (3.13 in floating point). Further, it should be noted that the filter tap coefficients C 0 -C 6  need not necessarily be symmetrical with respect to the center pixel P 3 .
 
     Vertical filtering is also applied by the noise reduction logic  934  subsequent to green non-uniformity correction and horizontal filtering processes. In one embodiment, the vertical filter operation may provide a 5-tap filter, as shown in  FIG. 75 , with the center tap of the vertical filter located at P 2 . The vertical filtering process may occur in a similar manner as the horizontal filtering process described above. For instance, gradients across the edge of each filter tap are computed, and if it is above a vertical edge threshold (vertTh), the filter tap is folded to the center pixel P 2 . The vertical filter may process the image data independently for each color component (R, B, Gr, Gb) and may use unfiltered values as inputs values. 
     Based upon the pixels shown in  FIG. 75 , vertical edge gradients for each filter tap may be calculated as follows:
 
 Ev 0=abs( P 0− P 1)  (71)
 
 Ev 1=abs( P 1− P 2)  (72)
 
 Ev 2=abs( P 2− P 3)  (73)
 
 Ev 3=abs( P 3− P 4)  (74)
 
The edge gradients Ev 0 -Ev 5  may then be utilized by the vertical filter to determine a vertical filtering output, P vert , using the formula shown in Equation 75 below:
 
 P   vert   =C 0×[( Ev 1&gt;vert Th[c ])? P 2:( Ev 0&gt;vert Th[c ])? P 1: P 0]+ C 1×[( Ev 1&gt;vert Th[c ])? P 2: P 1]+ C 2× P 2+ C 3×[( Ev 2&gt;vert Th[c ])? P 2: P 3]+ C 4×[( Ev 2&gt;vert Th[c ])? P 2:( Eh 3&gt;vert Th[c ])? P 3: P 4],  (75)
 
wherein vertTh[c] is the vertical edge threshold for each color component c (e.g., R, B, Gr, and Gb), and wherein C 0 -C 4  are the filter tap coefficients corresponding to the pixels P 0 -P 4  of  FIG. 75 , respectively. The vertical filter output P vert  may be applied at the center pixel P 2  location. In one embodiment, the filter tap coefficients C 0 -C 4  may be 16-bit two&#39;s complement values with 3 integer bits and 13 fractional bits (3.13 in floating point). Further, it should be noted that the filter tap coefficients C 0 -C 4  need not necessarily be symmetrical with respect to the center pixel P 2 .
 
     Additionally, with regard to boundary conditions, when neighboring pixels are outside of the raw frame  278  ( FIG. 19 ), the values of the out-of-bound pixels are replicated with the value of same color pixel at the edge of the raw frame. This convention may be implemented for both horizontal and vertical filtering operations. By way of example, referring again to  FIG. 74 , in the case of horizontal filtering, if the pixel P 2  is an edge pixel at the left-most edge of the raw frame, and the pixels P 0  and P 1  are outside of the raw frame, then the values of the pixels P 0  and P 1  are substituted with the value of the pixel P 2  for horizontal filtering. 
     Referring again back to the block diagram of the raw processing logic  900  shown in  FIG. 68 , the output of the noise reduction logic  934  is subsequently sent to the lens shading correction (LSC) logic  936  for processing. As discussed above, lens shading correction techniques may include applying an appropriate gain on a per-pixel basis to compensate for drop-offs in light intensity, which may be the result of the geometric optics of the lens, imperfections in manufacturing, misalignment of the microlens array and the color array filter, and so forth. Further, the infrared (IR) filter in some lenses may cause the drop-off to be illuminant-dependent and, thus, lens shading gains may be adapted depending upon the light source detected. 
     In the depicted embodiment, the LSC logic  936  of the ISP pipe  82  may be implemented in a similar manner, and thus provide generally the same functions, as the LSC logic  464  of the ISP front-end block  80 , as discussed above with reference to  FIGS. 40-48 . Accordingly, in order to avoid redundancy, it should be understood that the LSC logic  936  of the presently illustrated embodiment is configured to operate in generally the same manner as the LSC logic  460  and, as such, the description of the lens shading correction techniques provided above will not be repeated here. However, to generally summarize, it should be understood that the LSC logic  936  may process each color component of the raw pixel data stream independently to determine a gain to apply to the current pixel. In accordance with the above-discussed embodiments, the lens shading correction gain may be determined based upon a defined set of gain grid points distributed across the imaging frame, wherein the interval between each grid point is defined by a number of pixels (e.g., 8 pixels, 16 pixels etc.). If the location of the current pixel corresponds to a grid point, then the gain value associated with that grid point is applied to the current pixel. However, if the location of the current pixel is between grid points (e.g., G 0 , G 1 , G 2 , and G 3  of  FIG. 43 ), then the LSC gain value may be calculated by interpolation of the grid points between which the current pixel is located (Equations 13a and 13b). This process is depicted by the process  528  of  FIG. 44 . Further, as mentioned above with respect to  FIG. 42 , in some embodiments, the grid points may be distributed unevenly (e.g., logarithmically), such that the grid points are less concentrated in the center of the LSC region  504 , but more concentrated towards the corners of the LSC region  504 , typically where lens shading distortion is more noticeable. 
     Additionally, as discussed above with reference to  FIGS. 47 and 48 , the LSC logic  936  may also apply a radial gain component with the grid gain values. The radial gain component may be determined based upon distance of the current pixel from the center of the image (Equations 14-16). As mentioned, using a radial gain allows for the use of single common gain grid for all color components, which may greatly reduce the total storage space required for storing separate gain grids for each color component. This reduction in grid gain data may decrease implementation costs, as grid gain data tables may account for a significant portion of memory or chip area in image processing hardware. 
     Next, referring again to the raw processing logic block diagram  900  of  FIG. 68 , the output of the LSC logic  936  is then passed to a second gain, offset, and clamping (GOC) block  938 . The GOC logic  938  may be applied prior to demosaicing (by logic block  940 ) and may be used to perform auto-white balance on the output of the LSC logic  936 . In the depicted embodiment, the GOC logic  938  may be implemented in the same manner as the GOC logic  930  (and the BLC logic  462 ). Thus, in accordance with the Equation 11 above, the input received by the GOC logic  938  is first offset by a signed value and then multiplied by a gain. The resulting value is then clipped to a minimum and a maximum range in accordance with Equation 12. 
     Thereafter, the output of the GOC logic  938  is forwarded to the demosaicing logic  940  for processing to produce a full color (RGB) image based upon the raw Bayer input data. As will be appreciated, the raw output of an image sensor using a color filter array, such as a Bayer filter is “incomplete” in the sense that each pixel is filtered to acquire only a single color component. Thus, the data collected for an individual pixel alone is insufficient to determine color. Accordingly, demosaicing techniques may be used to generate a full color image from the raw Bayer data by interpolating the missing color data for each pixel. 
     Referring now to  FIG. 76 , a graphical process flow  692  that provides a general overview as to how demosaicing may be applied to a raw Bayer image pattern  1034  to produce a full color RGB is illustrated. As shown, a 4×4 portion  1036  of the raw Bayer image  1034  may include separate channels for each color component, including a green channel  1038 , a red channel  1040 , and a blue channel  1042 . Because each imaging pixel in a Bayer sensor only acquires data for one color, the color data for each color channel  1038 ,  1040 , and  1042  may be incomplete, as indicated by the “?” symbols. By applying a demosaicing technique  1044 , the missing color samples from each channel may be interpolated. For instance, as shown by reference number  1046 , interpolated data G′ may be used to fill the missing samples on the green color channel Similarly, interpolated data R′ may (in combination with the interpolated data G′  1046 ) be used to fill the missing samples on the red color channel  1048 , and interpolated data B′may (in combination with the interpolated data G′  1046 ) be used to fill the missing samples on the blue color channel  1050 . Thus, as a result of the demosaicing process, each color channel (R, G, B) will have a full set of color data, which may then be used to reconstruct a full color RGB image  1052 . 
     A demosaicing technique that may be implemented by the demosaicing logic  940  will now be described in accordance with one embodiment. On the green color channel, missing color samples may be interpolated using a low pass directional filter on known green samples and a high pass (or gradient) filter on the adjacent color channels (e.g., red and blue). For the red and blue color channels, the missing color samples may be interpolated in a similar manner, but by using low pass filtering on known red or blue values and high pass filtering on co-located interpolated green values. Further, in one embodiment, demosaicing on the green color channel may utilize a 5×5 pixel block edge-adaptive filter based on the original Bayer color data. As will be discussed further below, the use of an edge-adaptive filter may provide for the continuous weighting based on gradients of horizontal and vertical filtered values, which reduce the appearance of certain artifacts, such as aliasing, “checkerboard,” or “rainbow” artifacts, commonly seen in conventional demosaicing techniques. 
     During demosaicing on the green channel, the original values for the green pixels (Gr and Gb pixels) of the Bayer image pattern are used. However, in order to obtain a full set of data for the green channel, green pixel values may be interpolated at the red and blue pixels of the Bayer image pattern. In accordance with the present technique, horizontal and vertical energy components, respectively referred to as Eh and Ev, are first calculated at red and blue pixels based on the above-mentioned 5×5 pixel block. The values of Eh and Ev may be used to obtain an edge-weighted filtered value from the horizontal and vertical filtering steps, as discussed further below. 
     By way of example,  FIG. 77  illustrates the computation of the Eh and Ev values for a red pixel centered in the 5×5 pixel block at location (j, i), wherein j corresponds to a row and i corresponds to a column. As shown, the calculation of Eh considers the middle three rows (j−1, j, j+1) of the 5×5 pixel block, and the calculation of Ev considers the middle three columns (i−1, i, i+1) of the 5×5 pixel block. To compute Eh, the absolute value of the sum of each of the pixels in the red columns (i−2, i, i+2) multiplied by a corresponding coefficient (e.g., −1 for columns i−2 and i+2; 2 for column i) is summed with the absolute value of the sum of each of the pixels in the blue columns (i−1, i+1) multiplied by a corresponding coefficient (e.g., 1 for column i−1; −1 for column i+1). To compute Ev, the absolute value of the sum of each of the pixels in the red rows (j−2, j, j+2) multiplied by a corresponding coefficient (e.g., −1 for rows j−2 and j+2; 2 for row j) is summed with the absolute value of the sum of each of the pixels in the blue rows (j−1, j+1) multiplied by a corresponding coefficient (e.g., 1 for row j−1; −1 for row j+1). These computations are illustrated by Equations 76 and 77 below:
 
 Eh =abs[2(( P ( j− 1, i )+ P ( j,i )+ P ( j+ 1, i ))−( P ( j− 1, i− 2)+ P ( j,i− 2)+ P ( j+ 1, i− 2))−( P ( j− 1, i+ 2)+ P ( j,i+ 2)+ P ( j+ 1, i+ 2)]+abs[( P ( j− 1, i− 1)+ P ( j,i− 1)+ P ( j+ 1, i− 1))−( P ( j− 1, i+ 1)+ P ( j,i+ 1)+ P ( j+ 1, i+ 1)]  (76)
 
 Ev =abs[2( P ( j,i− 1)+ P ( j,i )+ P ( j,i+ 1))−( P ( j− 2, i− 1)+ P ( j− 2, i )+ P ( j− 2, i+ 1))−( P ( j+ 2, i− 1)+ P ( j+ 2, i )+ P ( j+ 2, i+ 1]+abs[( P ( j− 1, i− 1)+ P ( j− 1, i )+ P ( j− 1, i+ 1))−( P ( j+ 1, i− 1)+ P ( j+ 1, i )+ P ( j+ 1, i+ 1)]  (77)
 
Thus, the total energy sum may be expressed as: Eh+Ev. Further, while the example shown in  FIG. 77  illustrates the computation of Eh and Ev for a red center pixel at (j, i), it should be understood that the Eh and Ev values may be determined in a similar manner for blue center pixels.
 
     Next, horizontal and vertical filtering may be applied to the Bayer pattern to obtain the vertical and horizontal filtered values Gh and Gv, which may represent interpolated green values in the horizontal and vertical directions, respectively. The filtered values Gh and Gv may be determined using a low pass filter on known neighboring green samples in addition to using directional gradients of the adjacent color (R or B) to obtain a high frequency signal at the locations of the missing green samples. For instance, with reference to  FIG. 78 , an example of horizontal interpolation for determining Gh will now be illustrated. 
     As shown in  FIG. 78 , five horizontal pixels (R 0 , G 1 , R 2 , G 3 , and R 4 ) of a red line  1060  of the Bayer image, wherein R 2  is assumed to be the center pixel at (j, i), may be considered in determining Gh. Filtering coefficients associated with each of these five pixels are indicated by reference numeral  1062 . Accordingly, the interpolation of a green value, referred to as G 2 ′, for the center pixel R 2 , may be determined as follows: 
                     G   ⁢           ⁢     2   ′       =           G   ⁢           ⁢   1     +     G   ⁢           ⁢   3       2     +         2   ⁢   R   ⁢           ⁢   2     -     (         R   ⁢           ⁢   0     +     R   ⁢           ⁢   2       2     )     -     (         R   ⁢           ⁢   2     +     R   ⁢           ⁢   4       2     )       2               (   78   )               
Various mathematical operations may then be utilized to produce the expression for G 2 ′ shown in Equations 79 and 80 below:
 
                     G   ⁢           ⁢     2   ′       =           2   ⁢   G   ⁢           ⁢   1     +     2   ⁢   G   ⁢           ⁢   3       4     +         4   ⁢   R   ⁢           ⁢   2     -     R   ⁢           ⁢   0     -     R   ⁢           ⁢   2     -     R   ⁢           ⁢   2     -     R   ⁢           ⁢   4       4               (   79   )                 G   ⁢           ⁢     2   ′       =         2   ⁢   G   ⁢           ⁢   1     +     2   ⁢   G   ⁢           ⁢   3     +     2   ⁢   R   ⁢           ⁢   2     -     R   ⁢           ⁢   0     -     R   ⁢           ⁢   4       4             (   80   )               
Thus, with reference to  FIG. 78  and the Equations 78-80 above, the general expression for the horizontal interpolation for the green value at (j, i) may be derived as:
 
     
       
         
           
             
               
                 
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     The vertical filtering component Gv may be determined in a similar manner as Gh. For example, referring to  FIG. 79 , five vertical pixels (R 0 , G 1 , R 2 , G 3 , and R 4 ) of a red column  1064  of the Bayer image and their respective filtering coefficients  1068 , wherein R 2  is assumed to be the center pixel at (j, i), may be considered in determining Gv. Using low pass filtering on the known green samples and high pass filtering on the red channel in the vertical direction, the following expression may be derived for Gv: 
                   Gv   =             (       2   ⁢     P   ⁡     (       j   -   1     ,   i     )         +     2   ⁢     P   ⁡     (       j   +   1     ,   i     )         +     2   ⁢     P   ⁡     (     j   ,   i     )         -                     P   ⁡     (       j   -   2     ,   i     )       -     P   ⁡     (       j   +   2     ,   i     )         )           4             (   82   )               
While the examples discussed herein have shown the interpolation of green values on a red pixel, it should be understood that the expressions set forth in Equations 81 and 82 may also be used in the horizontal and vertical interpolation of green values for blue pixels.
 
     The final interpolated green value G′ for the center pixel (j, i) may be determined by weighting the horizontal and vertical filter outputs (Gh and Gv) by the energy components (Eh and Ev) discussed above to yield the following equation: 
                       G   ′     ⁡     (     j   ,   i     )       =         (     Ev     Eh   +   Ev       )     ⁢   Gh     +       (     Eh     Eh   +   Ev       )     ⁢   Gv               (   83   )               
As discussed above, the energy components Eh and Ev may provide for edge-adaptive weighting of the horizontal and vertical filter outputs Gh and Gv, which may help to reduce image artifacts, such as rainbow, aliasing, or checkerboard artifacts, in the reconstructed RGB image. Additionally, the demosaicing logic  940  may provide an option to bypass the edge-adaptive weighting feature by setting the Eh and Ev values each to 1, such that Gh and Gv are equally weighted.
 
     In one embodiment, the horizontal and vertical weighting coefficients, shown in Equation 51 above, may be quantized to reduce the precision of the weighting coefficients to a set of “coarse” values. For instance, in one embodiment, the weighting coefficients may be quantized to eight possible weight ratios: 1/8, 2/8, 3/8, 4/8, 5/8, 6/8, 7/8, and 8/8. Other embodiments may quantize the weighting coefficients into 16 values (e.g., 1/16 to 16/16), 32 values (1/32 to 32/32), and so forth. As can be appreciated, when compared to using full precision values (e.g., 32-bit floating point values), the quantization of the weight coefficients may reduce the implementation complexity when determining and applying the weighting coefficients to horizontal and vertical filter outputs. 
     In further embodiments, the presently disclosed techniques, in addition to determining and using horizontal and vertical energy components to apply weighting coefficients to the horizontal (Gh) and vertical (Gv) filtered values, may also determine and utilize energy components in the diagonal-positive and diagonal-negative directions. For instance, in such embodiments, filtering may also be applied in the diagonal-positive and diagonal-negative directions. Weighting of the filter outputs may include selecting the two highest energy components, and using the selected energy components to weight their respective filter outputs. For example, assuming that the two highest energy components correspond to the vertical and diagonal-positive directions, the vertical and diagonal-positive energy components are used to weight the vertical and diagonal-positive filter outputs to determine the interpolated green value (e.g., at a red or blue pixel location in the Bayer pattern). 
     Next, demosaicing on the red and blue color channels may be performed by interpolating red and blue values at the green pixels of the Bayer image pattern, interpolating red values at the blue pixels of the Bayer image pattern, and interpolating blue values at the red pixels of the Bayer image pattern. In accordance with the present discussed techniques, missing red and blue pixel values may be interpolated using low pass filtering based upon known neighboring red and blue pixels and high pass filtering based upon co-located green pixel values, which may be original or interpolated values (from the green channel demosaicing process discussed above) depending on the location of the current pixel. Thus, with regard to such embodiments, it should be understood that interpolation of missing green values may be performed first, such that a complete set of green values (both original and interpolated values) is available when interpolating the missing red and blue samples. 
     The interpolation of red and blue pixel values may be described with reference to  FIG. 80 , which illustrates various 3×3 blocks of the Bayer image pattern to which red and blue demosaicing may be applied, as well as interpolated green values (designated by G′) that may have been obtained during demosaicing on the green channel. Referring first to block  1070 , the interpolated red value, R′ 11 , for the Gr pixel (G 11 ) may be determined as follows: 
                       R   11   ′     =         (       R   10     +     R   12       )     2     +       (       2   ⁢           ⁢     G   11       -     G   10   ′     -     G   12   ′       )     2         ,           (   84   )               
where G′ 10  and G′ 12  represent interpolated green values, as shown by reference number  1078 .
 
     Similarly, the interpolated blue value, B′ 11 , for the Gr pixel (G 11 ) may be determined as follows: 
                       B   11   ′     =         (       B   01     +     B   21       )     2     +       (       2   ⁢           ⁢     G   11       -     G   01   ′     -     G   21   ′       )     2         ,           (   85   )               
wherein G′ 01  and G′ 21  represent interpolated green values ( 1078 ).
 
     Next, referring to the pixel block  1072 , in which the center pixel is a Gb pixel (G 11 ), the interpolated red value, R′ 11 , and blue value B′ 11 , may be determined as shown in Equations 86 and 87 below: 
     
       
         
           
             
               
                 
                   
                     R 
                     11 
                     ′ 
                   
                   = 
                   
                     
                       
                         ( 
                         
                           
                             R 
                             01 
                           
                           + 
                           
                             R 
                             21 
                           
                         
                         ) 
                       
                       2 
                     
                     + 
                     
                       
                         ( 
                         
                           
                             2 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             
                               G 
                               11 
                             
                           
                           - 
                           
                             G 
                             01 
                             ′ 
                           
                           - 
                           
                             G 
                             21 
                             ′ 
                           
                         
                         ) 
                       
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   86 
                   ) 
                 
               
             
             
               
                 
                   
                     B 
                     11 
                     ′ 
                   
                   = 
                   
                     
                       
                         ( 
                         
                           
                             B 
                             10 
                           
                           + 
                           
                             B 
                             12 
                           
                         
                         ) 
                       
                       2 
                     
                     + 
                     
                       
                         ( 
                         
                           
                             2 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             
                               G 
                               11 
                             
                           
                           - 
                           
                             G 
                             10 
                             ′ 
                           
                           - 
                           
                             G 
                             12 
                             ′ 
                           
                         
                         ) 
                       
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   87 
                   ) 
                 
               
             
           
         
       
     
     Further, referring to pixel block  1074 , the interpolation of a red value on a blue pixel, B 11 , may be determined as follows: 
                       R   11   ′     =         (       R   00     +     R   02     +     R   20     +     R   22       )     4     +       (       4   ⁢           ⁢     G   11   ′       -     G   00   ′     -     G   02   ′     -     G   20   ′     -     G   22   ′       )     4         ,           (   88   )               
wherein G′ 00 , G′ 02 , G′ 11 , G′ 20 , and G′ 22  represent interpolated green values, as shown by reference number  1080 . Finally, the interpolation of a blue value on a red pixel, as shown by pixel block  1076 , may be calculated as follows:
 
     
       
         
           
             
               
                 
                   
                     B 
                     11 
                     ′ 
                   
                   = 
                   
                     
                       
                         ( 
                         
                           
                             B 
                             00 
                           
                           + 
                           
                             B 
                             02 
                           
                           + 
                           
                             B 
                             20 
                           
                           + 
                           
                             B 
                             22 
                           
                         
                         ) 
                       
                       4 
                     
                     + 
                     
                       
                         ( 
                         
                           
                             4 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             
                               G 
                               11 
                               ′ 
                             
                           
                           - 
                           
                             G 
                             00 
                             ′ 
                           
                           - 
                           
                             G 
                             02 
                             ′ 
                           
                           - 
                           
                             G 
                             20 
                             ′ 
                           
                           - 
                           
                             G 
                             22 
                             ′ 
                           
                         
                         ) 
                       
                       4 
                     
                   
                 
               
               
                 
                   ( 
                   89 
                   ) 
                 
               
             
           
         
       
     
     While the embodiment discussed above relied on color differences (e.g., gradients) for determining red and blue interpolated values, another embodiment may provide for interpolated red and blue values using color ratios. For instance, interpolated green values (blocks  1078  and  1080 ) may be used to obtain a color ratio at red and blue pixel locations of the Bayer image pattern, and linear interpolation of the ratios may be used to determine an interpolated color ratio for the missing color sample. The green value, which may be an interpolated or an original value, may be multiplied by the interpolated color ratio to obtain a final interpolated color value. For instance, interpolation of red and blue pixel values using color ratios may be performed in accordance with the formulas below, wherein Equations 90 and 91 show the interpolation of red and blue values for a Gr pixel, Equations 92 and 93 show the interpolation of red and blue values for a Gb pixel, Equation 94 shows the interpolation of a red value on a blue pixel, and Equation 95 shows the interpolation of a blue value on a red pixel: 
     
       
         
           
             
               
                 
                   
                     
                       R 
                       11 
                       ′ 
                     
                     = 
                     
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                         
                           
                             ( 
                             
                               
                                 R 
                                 10 
                               
                               
                                 G 
                                 10 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 R 
                                 12 
                               
                               
                                 G 
                                 12 
                                 ′ 
                               
                             
                             ) 
                           
                         
                         2 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         R 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       interpolated 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       when 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       is 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       a 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       Gr 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       pixel 
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   90 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       B 
                       11 
                       ′ 
                     
                     = 
                     
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                         
                           
                             ( 
                             
                               
                                 B 
                                 01 
                               
                               
                                 G 
                                 01 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 B 
                                 21 
                               
                               
                                 G 
                                 21 
                                 ′ 
                               
                             
                             ) 
                           
                         
                         2 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         B 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       interpolated 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       when 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       is 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       a 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       Gr 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       pixel 
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   91 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       R 
                       11 
                       ′ 
                     
                     = 
                     
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                         
                           
                             ( 
                             
                               
                                 R 
                                 01 
                               
                               
                                 G 
                                 01 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 R 
                                 21 
                               
                               
                                 G 
                                 21 
                                 ′ 
                               
                             
                             ) 
                           
                         
                         2 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         R 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       interpolated 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       when 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       is 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       a 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       Gb 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       pixel 
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   92 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       B 
                       11 
                       ′ 
                     
                     = 
                     
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                         
                           
                             ( 
                             
                               
                                 B 
                                 10 
                               
                               
                                 G 
                                 10 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 B 
                                 12 
                               
                               
                                 G 
                                 12 
                                 ′ 
                               
                             
                             ) 
                           
                         
                         2 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         B 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       interpolated 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       when 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         G 
                         11 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       is 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       a 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       Gb 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       pixel 
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   93 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       R 
                       11 
                       ′ 
                     
                     = 
                     
                       
                         G 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                         
                           
                             ( 
                             
                               
                                 R 
                                 00 
                               
                               
                                 G 
                                 00 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 R 
                                 02 
                               
                               
                                 G 
                                 02 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 R 
                                 20 
                               
                               
                                 G 
                                 20 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 R 
                                 22 
                               
                               
                                 G 
                                 22 
                                 ′ 
                               
                             
                             ) 
                           
                         
                         4 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         R 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       interpolated 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       on 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       a 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       blue 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       pixel 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         B 
                         11 
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   94 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       B 
                       11 
                       ′ 
                     
                     = 
                     
                       
                         G 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                         
                           
                             ( 
                             
                               
                                 B 
                                 00 
                               
                               
                                 G 
                                 00 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 B 
                                 02 
                               
                               
                                 G 
                                 02 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 B 
                                 20 
                               
                               
                                 G 
                                 20 
                                 ′ 
                               
                             
                             ) 
                           
                           + 
                           
                             ( 
                             
                               
                                 B 
                                 22 
                               
                               
                                 G 
                                 22 
                                 ′ 
                               
                             
                             ) 
                           
                         
                         4 
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         B 
                         11 
                         ′ 
                       
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       interpolated 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       on 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       a 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       red 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       pixel 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       
                         B 
                         11 
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   95 
                   ) 
                 
               
             
           
         
       
     
     Once the missing color samples have been interpolated for each image pixel from the Bayer image pattern, a complete sample of color values for each of the red, blue, and green color channels (e.g.,  1046 ,  1048 , and  1050  of  FIG. 76 ) may be combined to produce a full color RGB image. For instance, referring back  FIGS. 49 and 50 , the output  910  of the raw pixel processing logic  900  may be an RGB image signal in 8, 10, 12 or 14-bit formats. 
     Referring now to  FIGS. 81-84 , various flow charts illustrating processes for demosaicing a raw Bayer image pattern in accordance with disclosed embodiments are illustrated. Specifically, the process  1082  of  FIG. 81  depicts the determination of which color components are to be interpolated for a given input pixel P. Based on the determination by process  1082 , one or more of the process  1100  ( FIG. 82 ) for interpolating a green value, the process  1112  ( FIG. 83 ) for interpolating a red value, or the process  1124  ( FIG. 84 ) for interpolating a blue value may be performed (e.g., by the demosaicing logic  940 ). 
     Beginning with  FIG. 81 , the process  1082  begins at step  1084  when an input pixel P is received. Decision logic  1086  determines the color of the input pixel. For instance, this may depend on the location of the pixel within the Bayer image pattern. Accordingly, if P is identified as being a green pixel (e.g., Gr or Gb), the process  1082  proceeds to step  1088  to obtain interpolated red and blue values for P. This may include, for example, continuing to the processes  1112  and  1124  of  FIGS. 83 and 84 , respectively. If P is identified as being a red pixel, then the process  1082  proceeds to step  1090  to obtain interpolated green and blue values for P. This may include further performing the processes  1100  and  1124  of  FIGS. 82 and 84 , respectively. Additionally, if P is identified as being a blue pixel, then the process  1082  proceeds to step  1092  to obtain interpolated green and red values for P. This may include further performing the processes  1100  and  1112  of  FIGS. 82 and 83 , respectively. Each of the processes  1100 ,  1112 , and  1124  are described further below. 
     The process  1100  for determining an interpolated green value for the input pixel P is illustrated in  FIG. 82  and includes steps  1102 - 1110 . At step  1102 , the input pixel P is received (e.g., from process  1082 ). Next, at step  1104 , a set of neighboring pixels forming a 5×5 pixel block is identified, with P being the center of the 5×5 block. Thereafter, the pixel block is analyzed to determine horizontal and vertical energy components at step  1106 . For instance, the horizontal and vertical energy components may be determined in accordance with Equations 76 and 77 for calculating Eh and Ev, respectively. As discussed, the energy components Eh and Ev may be used as weighting coefficients to provide edge-adaptive filtering and, therefore, reduce the appearance of certain demosaicing artifacts in the final image. At step  1108 , low pass filtering and high pass filtering as applied in horizontal and vertical directions to determine horizontal and vertical filtering outputs. For example, the horizontal and vertical filtering outputs, Gh and Gv, may be calculated in accordance with Equations 81 and 82. Next the process  1082  continues to step  1110 , at which the interpolated green value G′ is interpolated based on the values of Gh and Gv weighted with the energy components Eh and Ev, as shown in Equation 83. 
     Next, with regard to the process  1112  of  FIG. 83 , the interpolation of red values may begin at step  1114 , at which the input pixel P is received (e.g., from process  1082 ). At step  1116 , a set of neighboring pixels forming a 3×3 pixel block is identified, with P being the center of the 3×3 block. Thereafter, low pass filtering is applied on neighboring red pixels within the 3×3 block at step  1118 , and high pass filtering is applied (step  1120 ) on co-located green neighboring values, which may be original green values captured by the Bayer image sensor, or interpolated values (e.g., determined via process  1100  of  FIG. 82 ). The interpolated red value R′ for P may be determined based on the low pass and high pass filtering outputs, as shown at step  1122 . Depending on the color of P, R′ may be determined in accordance with one of the Equations 84, 86, or 88. 
     With regard to the interpolation of blue values, the process  1124  of  FIG. 84  may be applied. The steps  1126  and  1128  are generally identical to the steps  1114  and  1116  of the process  1112  ( FIG. 83 ). At step  1130 , low pass filtering is applied on neighboring blue pixels within the 3×3, and, at step  1132 , high pass filtering is applied on co-located green neighboring values, which may be original green values captured by the Bayer image sensor, or interpolated values (e.g., determined via process  1100  of  FIG. 82 ). The interpolated blue value B′ for P may be determined based on the low pass and high pass filtering outputs, as shown at step  1134 . Depending on the color of P, B′ may be determined in accordance with one of the Equations 85, 87, or 89. Further, as mentioned above, the interpolation of red and blue values may be determined using color differences (Equations 84-89) or color ratios (Equations 90-95). Again, it should be understood that interpolation of missing green values may be performed first, such that a complete set of green values (both original and interpolated values) is available when interpolating the missing red and blue samples. For example, the process  1100  of  FIG. 82  may be applied to interpolate all missing green color samples before performing the processes  1112  and  1124  of  FIGS. 83 and 84 , respectively. 
     Referring to  FIGS. 85-88 , examples of colored drawings of images processed by the raw pixel processing logic  900  in the ISP pipe  82  are provided.  FIG. 85  depicts an original image scene  1140 , which may be captured by the image sensor  90  of the imaging device  30 .  FIG. 86  shows a raw Bayer image  1142  which may represent the raw pixel data captured by the image sensor  90 . As mentioned above, conventional demosaicing techniques may not provide for adaptive filtering based on the detection of edges (e.g., borders between areas of two or more colors) in the image data, which may, undesirably, produce artifacts in the resulting reconstructed full color RGB image. For instance,  FIG. 87  shows an RGB image  1144  reconstructed using conventional demosaicing techniques, and may include artifacts, such as “checkerboard” artifacts  1146  at the edge  1148 . However, comparing the image  1144  to the RGB image  1150  of  FIG. 88 , which may be an example of an image reconstructed using the demosaicing techniques described above, it can be seen that the checkerboard artifacts  1146  present in  FIG. 87  are not present, or at least their appearance is substantially reduced at the edge  1148 . Thus, the images shown in  FIGS. 85-88  are intended to illustrate at least one advantage that the demosaicing techniques disclosed herein have over conventional methods. 
     Referring back to  FIG. 67 , having now thoroughly described the operation of the raw pixel processing logic  900 , which may output an RGB image signal  910 , the present discussion will now focus on describing the processing of the RGB image signal  910  by the RGB processing logic  902 . As shown the RGB image signal  910  may be sent to the selection logic  914  and/or to the memory  108 . The RGB processing logic  902  may receive the input signal  916 , which may be RGB image data from the signal  910  or from the memory  108 , as shown by signal  912 , depending on the configuration of the selection logic  914 . The RGB image data  916  may be processed by the RGB processing logic  902  to perform color adjustments operations, including color correction (e.g., using a color correction matrix), the application of color gains for auto-white balancing, as well as global tone mapping, and so forth. 
     A block diagram depicting a more detailed view of an embodiment of the RGB processing logic  902  is illustrated in  FIG. 89 . As shown, the RGB processing logic  902  includes the gain, offset, and clamping (GOC) logic  1160 , the RGB color correction logic  1162 , the GOC logic  1164 , the RGB gamma adjustment logic, and the color space conversion logic  1168 . The input signal  916  is first received by the gain, offset, and clamping (GOC) logic  1160 . In the illustrated embodiment, the GOC logic  1160  may apply gains to perform auto-white balancing on one or more of the R, G, or B color channels before processing by the color correction logic  1162 . 
     The GOC logic  1160  may be similar to the GOC logic  930  of the raw pixel processing logic  900 , except that the color components of the RGB domain are processed, rather the R, B, Gr, and Gb components of the Bayer image data. In operation, the input value for the current pixel is first offset by a signed value O[c] and multiplied by a gain G[c], as shown in Equation 11 above, wherein c represents the R, G, and B. As discussed above, the gain G[c] may be a 16-bit unsigned number with 2 integer bits and 14 fraction bits (e.g., 2.14 floating point representation), and the values for the gain G[c] may be previously determined during statistics processing (e.g., in the ISP front-end block  80 ). The computed pixel value Y (based on Equation 11) is then be clipped to a minimum and a maximum range in accordance with Equation 12. As discussed above, the variables min[c] and max[c] may represent signed 16-bit “clipping values” for the minimum and maximum output values, respectively. In one embodiment, the GOC logic  1160  may also be configured to maintain a count of the number of pixels that were clipped above and below maximum and minimum, respectively, for each color component R, G, and B. 
     The output of the GOC logic  1160  is then forwarded to the color correction logic  1162 . In accordance with the presently disclosed techniques, the color correction logic  1162  may be configured to apply color correction to the RGB image data using a color correction matrix (CCM). In one embodiment, the CCM may be a 3×3 RGB transform matrix, although matrices of other dimensions may also be utilized in other embodiments (e.g., 4×3, etc.). Accordingly, the process of performing color correction on an input pixel having R, G, and B components may be expressed as follows: 
                       [           R   ′           G   ′           B   ′           ]     =       [           CCM   ⁢           ⁢   00           CCM   ⁢           ⁢   01           CCM   ⁢           ⁢   02               CCM   ⁢           ⁢   10           CCM   ⁢           ⁢   11           CCM   ⁢           ⁢   12               CCM   ⁢           ⁢   20           CCM   ⁢           ⁢   21           CCM   ⁢           ⁢   22           ]     ×     [         R       G       B         ]         ,           (   96   )               
wherein R, G, and B represent the current red, green, and blue values for the input pixel, CCM 00 -CCM 22  represent the coefficients of the color correction matrix, and R′, G′, and B′ represent the corrected red, green, and blue values for the input pixel. Accordingly, the correct color values may be computed in accordance with Equations 97-99 below:
 
 R ′=(CCM00× R )+(CCM01× G )+(CCM02× B )  (97)
 
 G ′=(CCM10× R )+(CCM11× G )+(CCM12× B )  (98)
 
 B ′=(CCM20× R )+(CCM21× G )+(CCM22× B )  (99)
 
     The coefficients (CCM 00 -CCM 22 ) of the CCM may be determined during statistics processing in the ISP front-end block  80 , as discussed above. In one embodiment, the coefficients for a given color channel may be selected such that the sum of those coefficients (e.g., CCM 00 , CCM 01 , and CCM 02  for red color correction) is equal to 1, which may help to maintain the brightness and color balance. Further, the coefficients are typically selected such that a positive gain is applied to the color being corrected. For instance, with red color correction, the coefficient CCM 00  may be greater than 1, while one or both of the coefficients CCM 01  and CCM 02  may be less than 1. Setting the coefficients in this manner may enhance the red (R) component in the resulting corrected R′ value while subtracting some of the blue (B) and green (G) component. As will be appreciated, this may address issues with color overlap that may occur during acquisition of the original Bayer image, as a portion of filtered light for a particular colored pixel may “bleed” into a neighboring pixel of a different color. In one embodiment, the coefficients of the CCM may be provided as 16-bit two&#39;s-complement numbers with 4 integer bits and 12 fraction bits (expressed in floating point as 4.12). Additionally, the color correction logic  1162  may provide for clipping of the computed corrected color values if the values exceed a maximum value or are below a minimum value. 
     The output of the RGB color correction logic  1162  is then passed to another GOC logic block  1164 . The GOC logic  1164  may be implemented in an identical manner as the GOC logic  1160  and, thus, a detailed description of the gain, offset, and clamping functions provided will not be repeated here. In one embodiment, the application of the GOC logic  1164  subsequent to color correction may provide for auto-white balance of the image data based on the corrected color values, and may also adjust sensor variations of the red-to-green and blue-to-green ratios. 
     Next, the output of the GOC logic  1164  is sent to the RGB gamma adjustment logic  1166  for further processing. For instance, the RGB gamma adjustment logic  1166  may provide for gamma correction, tone mapping, histogram matching, and so forth. In accordance with disclosed embodiments, the gamma adjustment logic  1166  may provide for a mapping of the input RGB values to corresponding output RGB values. For instance, the gamma adjustment logic may provide for a set of three lookup tables, one table for each of the R, G, and B components. By way of example, each lookup table may be configured to store 256 entries of 10-bit values, each value representing an output level. The table entries may be evenly distributed in the range of the input pixel values, such that when the input value falls between two entries, the output value may be linearly interpolated. In one embodiment, each of the three lookup tables for R, G, and B may be duplicated, such that the lookup tables are “double buffered” in memory, thus allowing for one table to be used during processing, while its duplicate is being updated. Based on the 10-bit output values discussed above, it should be noted that the 14-bit RGB image signal is effectively down-sampled to 10 bits as a result of the gamma correction process in the present embodiment. 
     The output of the gamma adjustment logic  1166  may be sent to the memory  108  and/or to the color space conversion logic  1168 . The color space conversion (CSC) logic  1168  may be configured to convert the RGB output from the gamma adjustment logic  1166  to the YCbCr format, in which Y represents a luma component, Cb represents a blue-difference chroma component, and Cr represents a red-difference chroma component, each of which may be in a 10-bit format as a result of bit-depth conversion of the RGB data from 14-bits to 10-bits during the gamma adjustment operation. As discussed above, in one embodiment, the RGB output of the gamma adjustment logic  1166  may be down-sampled to 10-bits and thus converted to 10-bit YCbCr values by the CSC logic  1168 , which may then be forwarded to the YCbCr processing logic  904 , which will be discussed further below. 
     The conversion from the RGB domain to the YCbCr color space may be performed using a color space conversion matrix (CSCM). For instance, in one embodiment, the CSCM may be a 3×3 transform matrix. The coefficients of the CSCM may be set in accordance with a known conversion equation, such as the BT.601 and BT.709 standards. Additionally, the CSCM coefficients may be flexible based on the desired range of input and outputs. Thus, in some embodiments, the CSCM coefficients may be determined and programmed based on data collected during statistics processing in the ISP front-end block  80 . 
     The process of performing YCbCr color space conversion on an RGB input pixel may be expressed as follows: 
                       [         Y       Cb       Cr         ]     =       [           CSCM   ⁢           ⁢   00           CSCM   ⁢           ⁢   01           CSCM   ⁢           ⁢   02               CSCM   ⁢           ⁢   10           CSCM   ⁢           ⁢   11           CSCM   ⁢           ⁢   12               CSCM   ⁢           ⁢   20           CSCM   ⁢           ⁢   21           CSCM   ⁢           ⁢   22           ]     ×     [         R       G       B         ]         ,           (   100   )               
wherein R, G, and B represent the current red, green, and blue values for the input pixel in 10-bit form (e.g., as processed by the gamma adjustment logic  1166 ), CSCM 00 -CSCM 22  represent the coefficients of the color space conversion matrix, and Y, Cb, and Cr represent the resulting luma, and chroma components for the input pixel. Accordingly, the values for Y, Cb, and Cr may be computed in accordance with Equations 101-103 below:
 
 Y =(CSCM00× R )+(CSCM01× G )+(CSCM02× B )  (101)
 
 Cb =(CSCM10× R )+(CSCM11× G )+(CSCM12× B )  (102)
 
 Cr =(CSCM20 ×R )+(CSCM21 ×G )+CSCM22 ×B )  (103)
 
Following the color space conversion operation, the resulting YCbCr values may be output from the CSC logic  1168  as the signal  918 , which may be processed by the YCbCr processing logic  904 , as will be discussed below.
 
     In one embodiment, the coefficients of the CSCM may be 16-bit two&#39;s-complement numbers with 4 integer bits and 12 fraction bits (4.12). In another embodiment, the CSC logic  1168  may further be configured to apply an offset to each of the Y, Cb, and Cr values, and to clip the resulting values to a minimum and maximum value. By way of example only, assuming that the YCbCr values are in 10-bit form, the offset may be in a range of −512 to 512, and the minimum and maximum values may be 0 and 1023, respectively. 
     Referring again back to the block diagram of the ISP pipe logic  82  in  FIG. 67 , the YCbCr signal  918  may be sent to the selection logic  922  and/or to the memory  108 . The YCbCr processing logic  904  may receive the input signal  924 , which may be YCbCr image data from the signal  918  or from the memory  108 , as shown by signal  920 , depending on the configuration of the selection logic  922 . The YCbCr image data  924  may then be processed by the YCbCr processing logic  904  for luma sharpening, chroma suppression, chroma noise reduction, chroma noise reduction, as well as brightness, contrast, and color adjustments, and so forth. Further, the YCbCr processing logic  904  may provide for gamma mapping and scaling of the processed image data in both horizontal and vertical directions. 
     A block diagram depicting a more detailed view of an embodiment of the YCbCr processing logic  904  is illustrated in  FIG. 90 . As shown, the YCbCr processing logic  904  includes the image sharpening logic  1170 , the logic  1172  for adjusting brightness, contrast, and/or color, the YCbCr gamma adjustment logic  1174 , the chroma decimation logic  1176 , and the scaling logic  1178 . The YCbCr processing logic  904  may be configured to process pixel data in 4:4:4, 4:2:2, or 4:2:0 formats using 1-plane, 2-plane, or 3-plane memory configurations. Further, in one embodiment, the YCbCr input signal  924  may provide luma and chroma information as 10-bit values. 
     As will be appreciated, the reference to 1-plane, 2-plane, or 3-plane refers to the number of imaging planes utilized in picture memory. For instance, in a 3-plane format, each of the Y, Cb, and Cr components may utilize separate respective memory planes. In a 2-plane format, a first plane may be provided for the luma component (Y), and a second plane that interleaves the Cb and Cr samples may be provided for the chroma components (Cb and Cr). In a 1-plane format, a single plane in memory is interleaved with the luma and chroma samples. 
     Further, with regard to the 4:4:4, 4:2:2, and 4:2:0 formats, it may be appreciated that the 4:4:4 format refers to a sampling format in which each of the three YCbCr components are sampled at the same rate. In a 4:2:2 format, the chroma components Cb and Cr are sub-sampled at half the sampling rate of the luma component Y, thus reducing the resolution of chroma components Cb and Cr by half in the horizontal direction. Similarly the 4:2:0 format subs-samples the chroma components Cb and Cr in both the vertical and horizontal directions. 
     The processing of the YCbCr information may occur within an active source region defined within a source buffer, wherein the active source region contains “valid” pixel data. For example, referring to  FIG. 91 , a source buffer  1180  having defined therein an active source region  1182  is illustrated. In the illustrated example, the source buffer may represent a 4:4:4 1-plane format providing source pixels of 10-bit values. The active source region  1182  may be specified individually for luma (Y) samples and chroma samples (Cb and Cr). Thus, it should be understood that the active source region  1182  may actually include multiple active source regions for the luma and chroma samples. The start of the active source regions  1182  for luma and chroma may be determined based on an offset from a base address (0,0) 1184 of the source buffer. For instance, a starting position (Lm_X, Lm_Y)  1186  for the luma active source region may be defined by an x-offset  1190  and a y-offset  1194  with respect to the base address  1184 . Similarly, a starting position (Ch_X, Ch_Y)  1188  for the chroma active source region may be defined by an x-offset  1192  and a y-offset  1196  with respect to the base address  1184 . It should be noted that in the present example, the y-offsets  1192  and  1196  for luma and chroma, respectively, may be equal. Based on the starting position  1186 , the luma active source region may be defined by a width  1193  and a height  1200 , each of which may represent the number of luma samples in the x and y directions, respectively. Additionally, based on the starting position  1188 , the chroma active source region may be defined by a width  1202  and a height  1204 , each of which may represent the number of chroma samples in the x and y directions, respectively. 
       FIG. 92  further provides an example showing how active source regions for luma and chroma samples may be determined in a two-plane format. For instance, as shown, the luma active source region  1182  may be defined in a first source buffer  1180  (having the base address  1184 ) by the area specified by the width  1193  and height  1200  with respect to the starting position  1186 . A chroma active source region  1208  may be defined in a second source buffer  1206  (having the base address  1184 ) as the area specified by the width  1202  and height  1204  relative to the starting position  1188 . 
     With the above points in mind and referring back to  FIG. 90 , the YCbCr signal  924  is first received by the image sharpening logic  1170 . The image sharpening logic  1170  may be configured to perform picture sharpening and edge enhancement processing to increase texture and edge details in the image. As will be appreciated, image sharpening may improve the perceived image resolution. However, it is generally desirable that existing noise in the image is not detected as texture and/or edges, and thus not amplified during the sharpening process. 
     In accordance with the present technique, the image sharpening logic  1170  may perform picture sharpening using a multi-scale unsharp mask filter on the luma (Y) component of the YCbCr signal. In one embodiment, two or more low pass Gaussian filters of difference scale sizes may be provided. For example, in an embodiment that provides two Gaussian filters, the output (e.g., Gaussian blurring) of a first Gaussian filter having a first radius (x) is subtracted from the output of a second Gaussian filter having a second radius (y), wherein x is greater than y, to generate an unsharp mask. Additional unsharp masks may also be obtained by subtracting the outputs of the Gaussian filters from the Y input. In certain embodiments, the technique may also provide adaptive coring threshold comparison operations that may be performed using the unsharp masks such that, based upon the results of the comparison(s), gain amounts may be added to a base image, which may be selected as the original Y input image or the output of one of the Gaussian filters, to generate a final output. 
     Referring to  FIG. 93 , block diagram depicting exemplary logic  1210  for performing image sharpening in accordance with embodiments of the presently disclosed techniques is illustrated. The logic  1210  represents a multi-scale unsharp filtering mask that may be applied to an input luma image Yin. For instance, as shown, Yin is received and processed by two low pass Gaussian filters  1212  (G 1 ) and  1214  (G 2 ). In the present example, the filter  1212  may be a 3×3 filter and the filter  1214  may be a 5×5 filter. It should be appreciated, however, that in additional embodiments, more than two Gaussian filters, including filters of different scales may also be used (e.g., 7×7, 9×9, etc.). As will be appreciated, due to the low pass filtering process, the high frequency components, which generally correspond to noise, may be removed from the outputs of the G 1  and G 2  to produce “unsharp” images (G 1 out and G 2 out). As will be discussed below, using an unsharp input image as a base image allows for noise reduction as part of the sharpening filter. 
     The 3×3 Gaussian filter  1212  and the 5×5 Gaussian filter  1214  may be defined as shown below: 
               G   ⁢           ⁢   1     =       [           G   ⁢           ⁢     1   1             G   ⁢           ⁢     1   1             G   ⁢           ⁢     1   1                 G   ⁢           ⁢     1   1             G   ⁢           ⁢     1   0             G   ⁢           ⁢     1   1                 G   ⁢           ⁢     1   1             G   ⁢           ⁢     1   1             G   ⁢           ⁢     1   1             ]     256                   G   ⁢           ⁢   2     =       [           G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2                 G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   2                 G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   0             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   2                 G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   1             G   ⁢           ⁢     2   2                 G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             G   ⁢           ⁢     2   2             ]     256           
By way of example only, the values of the Gaussian filters G 1  and G 2  may be selected in one embodiment as follows:
 
     
       
         
           
             
               G 
               ⁢ 
               
                   
               
               ⁢ 
               1 
             
             = 
             
               
                 [ 
                 
                   
                     
                       28 
                     
                     
                       28 
                     
                     
                       28 
                     
                   
                   
                     
                       28 
                     
                     
                       32 
                     
                     
                       28 
                     
                   
                   
                     
                       28 
                     
                     
                       28 
                     
                     
                       28 
                     
                   
                 
                 ] 
               
               256 
             
           
         
       
       
         
           
             
               G 
               ⁢ 
               
                   
               
               ⁢ 
               2 
             
             = 
             
               
                 [ 
                 
                   
                     
                       9 
                     
                     
                       9 
                     
                     
                       9 
                     
                     
                       9 
                     
                     
                       9 
                     
                   
                   
                     
                       9 
                     
                     
                       12 
                     
                     
                       12 
                     
                     
                       12 
                     
                     
                       9 
                     
                   
                   
                     
                       9 
                     
                     
                       12 
                     
                     
                       16 
                     
                     
                       12 
                     
                     
                       9 
                     
                   
                   
                     
                       9 
                     
                     
                       12 
                     
                     
                       12 
                     
                     
                       12 
                     
                     
                       9 
                     
                   
                   
                     
                       9 
                     
                     
                       9 
                     
                     
                       9 
                     
                     
                       9 
                     
                     
                       9 
                     
                   
                 
                 ] 
               
               256 
             
           
         
       
     
     Based on Yin, G 1 out, and G 2 out, three unsharp masks, Sharp 1 , Sharp 2 , and Sharp 3 , may be generated. Sharp 1  may be determined as the unsharp image G 2 out of the Gaussian filter  1214  subtracted from the unsharp image G 1 out of the Gaussian filter  1212 . Because Sharp 1  is essentially the difference between two low pass filters, it may be referred to as a “mid band” mask, since the higher frequency noise components are already filtered out in the G 1 out and G 2 out unsharp images. Additionally, Sharp 2  may be calculated by subtracting G 2 out from the input luma image Yin, and Sharp 3  may be calculated by subtracting G 1 out from the input luma image Yin. As will be discussed below, an adaptive threshold coring scheme may be applied using the unsharp masks Sharp 1 , Sharp 2 , and Sharp 3 . 
     Referring to the selection logic  1216 , a base image may be selected based upon a control signal UnsharpSel. In the illustrated embodiment, the base image may be either the input image Yin, or the filtered outputs G 1 out or G 2 out. As will be appreciated, when an original images has a high noise variance (e.g., almost as high as the signal variance), using the original image Yin as the base image in sharpening may not sufficiently provide for reduction of the noise components during sharpening. Accordingly, when a particular threshold of noise content is detected in the input image, the selection logic  1216  may be adapted to select one of the low pass filtered outputs G 1 out or G 2 out from which high frequency content, which may include noise, has been reduced. In one embodiment, the value of the control signal UnsharpSel may be determined by analyzing statistical data acquired during statistics processing in the ISP front-end block  80  to determine the noise content of the image. By way of example, if the input image Yin has a low noise content, such that the appearance noise will likely not increase as a result of the sharpening process, the input image Yin may be selected as the base image (e.g., UnsharpSel=0). If the input image Yin is determined to contain a noticeable level of noise, such that the sharpening process may amplify the noise, one of the filtered images G 1 out or G 2 out may be selected (e.g., UnsharpSel=1 or 2, respectively). Thus, by applying an adaptive technique for selecting a base image, the logic  1210  essentially provides a noise reduction function. 
     Next, gains may be applied to one or more of the Sharp 1 , Sharp 2 , and Sharp 3  masks in accordance with an adaptive coring threshold scheme, as described below. Next, the unsharp values Sharp 1 , Sharp 2 , and Sharp 3  may be compared to various thresholds SharpThd 1 , SharpThd 2 , and SharpThd 3  (not necessarily respectively) by way of the comparator blocks  1218 ,  1220 , and  1222 . For instance, Sharp 1  value is always compared to SharpThd 1  at the comparator block  1218 . With respective to the comparator block  1220 , the threshold SharpThd 2  may be compared against either Sharp 1  or Sharp 2 , depending upon the selection logic  1226 . For instance, the selection logic  1226  may select Sharp 1  or Sharp 2  depending on the state of a control signal SharpCmp 2  (e.g., SharpCmp 2 =1 selects Sharp 1 ; SharpCmp 2 =0 selects Sharp 2 ). For example, in one embodiment, the state of SharpCmp 2  may be determined depending on the noise variance/content of the input image (Yin). 
     In the illustrated embodiment, it is generally preferable to set the SharpCmp 2  and SharpCmp 3  values to select Sharp 1 , unless it is detected that the image data has relatively low amounts of noise. This is because Sharp 1 , being the difference between the outputs of the Gaussian low pass filters G 1  and G 2 , is generally less sensitive to noise, and thus may help reduce the amount to which SharpAmt 1 , SharpAmt 2 , and SharpAmt 3  values vary due to noise level fluctuations in “noisy” image data. For instance, if the original image has a high noise variance, some of the high frequency components may not be caught when using fixed thresholds and, thus, may be amplified during the sharpening process. Accordingly, if the noise content of the input image is high, then some of the noise content may be present in Sharp 2 . In such instances, SharpCmp 2  may be set to 1 to select the mid-band mask Sharp 1  which, as discussed above, has reduced high frequency content due to being the difference of two low pass filter outputs and is thus less sensitive to noise. 
     As will be appreciated, a similar process may be applied to the selection of either Sharp 1  or Sharp 3  by the selection logic  1224  under the control of SharpCmp 3 . In one embodiment, SharpCmp 2  and SharpCmp 3  may be set to 1 by default (e.g., use Sharp 1 ), and set to 0 only for those input images that are identified as having generally low noise variances. This essentially provides an adaptive coring threshold scheme in which the selection of the comparison value (Sharp 1 , Sharp 2 , or Sharp 3 ) is adaptive based upon the noise variance of an input image. 
     Based on the outputs of the comparator blocks  1218 ,  1220 , and  1222 , the sharpened output image Ysharp may be determined by applying gained unsharp masks to the base image (e.g., selected via logic  1216 ). For instance, referring first to the comparator block  1222 , SharpThd 3  is compared to the B-input provided by selection logic  1224 , which shall be referred to herein as “SharpAbs,” and may be equal to either Sharp 1  or Sharp 3  depending on the state of SharpCmp 3 . If SharpAbs is greater than the threshold SharpThd 3 , then a gain SharpAmt 3  is applied to Sharp 3 , and the resulting value is added to the base image. If SharpAbs is less than the threshold SharpThd 3 , then an attenuated gain Att 3  may be applied. In one embodiment, the attenuated gain Att 3  may be determined as follows: 
                     Att   ⁢           ⁢   3     =       SharpAmt   ⁢           ⁢   3   ×   SharpAbs       SharpThd   ⁢           ⁢   3               (   104   )               
wherein, SharpAbs is either Sharp 1  or Sharp 3 , as determined by the selection logic  1224 . The selection of the based image summed with either the full gain (SharpAmt 3 ) or the attenuated gain (Att 3 ) is performed by the selection logic  1228  based upon the output of the comparator block  1222 . As will be appreciated, the use of an attenuated gain may address situations in which SharpAbs is not greater than the threshold (e.g., SharpThd 3 ), but the noise variance of the image is nonetheless close to the given threshold. This may help to reduce noticeable transitions between a sharp and an unsharp pixel. For instance, if the image data is passed without the attenuated gain in such circumstance, the resulting pixel may appear as a defective pixel (e.g., a stuck pixel).
 
     Next, a similar process may be applied with respect to the comparator block  1220 . For instance, depending on the state of SharpCmp 2 , the selection logic  1226  may provide either Sharp 1  or Sharp 2  as the input to the comparator block  1220  that is compared against the threshold SharpThd 2 . Depending on the output of the comparator block  1220 , either the gain SharpAmt 2  or an attenuated gain based upon SharpAmt 2 , Att 2 , is applied to Sharp 2  and added to the output of the selection logic  1228  discussed above. As will be appreciated, the attenuated gain Att 2  may be computed in a manner similar to Equation 104 above, except that the gain SharpAmt 2  and the threshold SharpThd 2  are applied with respect to SharpAbs, which may be selected as Sharp 1  or Sharp 2 . 
     Thereafter, a gain SharpAmt 1  or an attenuated gain Att 1  is applied to Sharp 1 , and the resulting value is summed with output of the selection logic  1230  to produce the sharpened pixel output Ysharp (from selection logic  1232 ). The selection of applying either the gain SharpAmt 1  or attenuated gain Att 1  may be determined based upon the output of the comparator block  1218 , which compares Sharp 1  against the threshold SharpThd 1 . Again, the attenuated gain Att 1  may be determined in a manner similar to Equation 104 above, except that the gain SharpAmt 1  and threshold SharpThd 1  are applied with respect to Sharp 1 . The resulting sharpened pixel values scaled using each of the three masks is added to the input pixel Yin to generate the sharpened output Ysharp which, in one embodiment, may be clipped to 10 bits (assuming YCbCr processing occurs at 10-bit precision). 
     As will be appreciated, when compared to conventional unsharp masking techniques, the image sharpening techniques set forth in this disclosure may provide for improving the enhancement of textures and edges while also reducing noise in the output image. In particular, the present techniques may be well-suited in applications in which images captured using, for example, CMOS image sensors, exhibit poor signal-to-noise ratio, such as images acquired under low lighting conditions using lower resolution cameras integrated into portable devices (e.g., mobile phones). For instance, when the noise variance and signal variance are comparable, it is difficult to use a fixed threshold for sharpening, as some of the noise components would be sharpened along with texture and edges. Accordingly, the techniques provided herein, as discussed above, may filter the noise from the input image using multi-scale Gaussian filters to extract features from the unsharp images (e.g., G 1 out and G 2 out) in order to provide a sharpened image that also exhibits reduced noise content. 
     Before continuing, it should be understood that the illustrated logic  1210  is intended to provide only one exemplary embodiment of the present technique. In other embodiments, additional or fewer features may be provided by the image sharpening logic  1170 . For instance, in some embodiments, rather than applying an attenuated gain, the logic  1210  may simply pass the base value. Additionally, some embodiments may not include the selection logic blocks  1224 ,  1226 , or  1216 . For instance, the comparator blocks  1220  and  1222  may simply receive the Sharp 2  and Sharp 3  values, respectively, rather than a selection output from the selection logic blocks  1224  and  1226 , respectively. While such embodiments may not provide for sharpening and/or noise reduction features that are as robust as the implementation shown in  FIG. 93 , it should be appreciated that such design choices may be the result of cost and/or business related constraints. 
     In the present embodiment, the image sharpening logic  1170  may also provide for edge enhancement and chroma suppression features once the sharpened image output YSharp is obtained. Each of these additional features will now be discussed below. Referring first to  FIG. 94 , exemplary logic  1234  for performing edge enhancement that may be implemented downstream from the sharpening logic  1210  of  FIG. 93  is illustrated in accordance with one embodiment. As shown, the original input value Yin is processed by a Sobel filter  1236  for edge detection. The Sobel filter  1236  may determine a gradient value YEdge based upon a 3×3 pixel block (referred to as “A” below) of the original image, with Yin being the center pixel of the 3×3 block. In one embodiment, the Sobel filter  1236  may calculate YEdge by convolving the original image data to detect changes in horizontal and vertical directions. This process is shown below in Equations 105-107. 
                       S   x     =     [         1       0         -   1             2       0         -   2             1       0         -   1           ]       ⁢     
     ⁢       S   y     =     [         1       2       1           0       0       0             -   1           -   2           -   1           ]       ⁢     
     ⁢         G   x     =       S   x     ×   A       ,             (   105   )                   G   y     =       S   y     ×   A       ,           (   106   )                 YEdge   =       G   x     ×     G   y         ,           (   107   )               
wherein S x  and S y  are represent matrix operators for gradient edge-strength detection in the horizontal and vertical directions, respectively, and wherein G x  and G y  represent gradient images that contain horizontal and vertical change derivatives, respectively. Accordingly, the output YEdge is determined as the product of G x  and G y .
 
     YEdge is then received by selection logic  1240  along with the mid-band Sharp 1  mask, as discussed above in  FIG. 93 . Based on the control signal EdgeCmp, either Sharp 1  or YEdge is compared to a threshold, EdgeThd, at the comparator block  1238 . The state of EdgeCmp may be determined, for example, based upon the noise content of an image, thus providing an adaptive coring threshold scheme for edge detection and enhancement. Next, the output of the comparator block  1238  may be provided to the selection logic  1242  and either a full gain or an attenuated gain may be applied. For instance, when the selected B-input to the comparator block  1238  (Sharp 1  or YEdge) is above EdgeThd, YEdge is multiplied by an edge gain, EdgeAmt, to determine the amount of edge enhancement that is to be applied. If the B-input at the comparator block  1238  is less than EdgeThd, then an attenuated edge gain, AttEdge, may be applied to avoid noticeable transitions between the edge enhanced and original pixel. As will be appreciated, AttEdge may be calculated in a similar manner as shown in Equation 104 above, but wherein EdgeAmt and EdgeThd are applied to “SharpAbs,” which may be Sharp 1  or YEdge, depending on the output of the selection logic  1240 . Thus, the edge pixel, enhanced using either the gain (EdgeAmt) or the attenuated gain (AttEdge) may be added to YSharp (output of logic  1210  of  FIG. 93 ) to obtain the edge-enhanced output pixel Yout which, in one embodiment, may be clipped to 10 bits (assuming YCbCr processing occurs at 10-bit precision). 
     With regard to chroma suppression features provided by the image sharpening logic  1170 , such features may attenuate chroma at luma edges. Generally, chroma suppression may be performed by applying a chroma gain (attenuation factor) of less than 1 depending on the value (YSharp, Yout) obtained from the luma sharpening and/or edge enhancement steps discussed above. By way of example,  FIG. 95  shows a graph  1250  that includes a curve  1252  representing chroma gains that may be selected for corresponding sharpened luma values (YSharp). The data represented by the graph  1250  may be implemented as a lookup table of YSharp values and corresponding chroma gains between 0 and 1 (an attenuation factor). The lookup tables are used to approximate the curve  1252 . For YSharp values that are co-located between two attenuation factors in the lookup table, linear interpolation may be applied to the two attenuation factors corresponding to YSharp values above and below the current YSharp value. Further, in other embodiments, the input luma value may also be selected as one of the Sharp 1 , Sharp 2 , or Sharp 3  values determined by the logic  1210 , as discussed above in  FIG. 93 , or the YEdge value determined by the logic  1234 , as discussed in  FIG. 94 . 
     Next, the output of the image sharpening logic  1170  ( FIG. 90 ) is processed by the brightness, contrast, and color (BCC) adjustment logic  1172 . A functional block diagram depicting an embodiment of the BCC adjustment logic  1172  is illustrated in  FIG. 96 . As shown the logic  1172  includes a brightness and contrast processing block  1262 , global hue control block  1264 , and a saturation control block  1266 . The presently illustrated embodiment provides for processing of the YCbCr data in 10-bit precision, although other embodiments may utilize different bit-depths. The functions of each of blocks  1262 ,  1264 , and  1266  are discussed below. 
     Referring first to the brightness and contrast processing block  1262 , an offset, YOffset, is first subtracted from the luma (Y) data to set the black level to zero. This is done to ensure that the contrast adjustment does not alter the black levels. Next, the luma value is multiplied by a contrast gain value to apply contrast control. By way of example, the contrast gain value may be a 12-bit unsigned with 2 integer bits and 10 fractional bits, thus providing for a contrast gain range of up to 4 times the pixel value. Thereafter, brightness adjustment may be implemented by adding (or subtracting) a brightness offset value from the luma data. By way of example, the brightness offset in the present embodiment may be a 10-bit two&#39;s complement value having a range of between −512 to +512. Further, it should be noted that brightness adjustment is performed subsequent to contrast adjustment in order to avoid varying the DC offset when changing contrast. Thereafter, the initial YOffset is added back to the adjusted luma data to re-position the black level. 
     Blocks  1264  and  1266  provide for color adjustment based upon hue characteristics of the Cb and Cr data. As shown, an offset of 512 (assuming 10-bit processing) is first subtracted from the Cb and Cr data to position the range to approximately zero. The hue is then adjusted in accordance with the following equations:
 
 Cb   adj   =Cb  cos(θ)+ Cr  sin(θ),  (108)
 
 Cr   adj   =Cr  cos(θ)− Cb  sin(θ),  (109)
 
wherein Cb adj  and Cr adj  represent adjusted Cb and Cr values, and wherein θ represents a hue angle, which may be calculated as follows:
 
                   θ   =     arctan   ⁡     (     Cr   Cb     )               (   110   )               
The above operations are depicted by the logic within the global hue control block  1264 , and may be represented by the following matrix operation:
 
                       [           Cb   adj               Cr   adj           ]     =       [         Ka       Kb             -   Kb         Ka         ]     ⁡     [         Cb           Cr         ]         ,           (   111   )               
wherein, Ka=cos(θ), Kb=sin(θ), and θ is defined above in Equation 110.
 
     Next, saturation control may be applied to the Cb adj  and Cr adj  values, as shown by the saturation control block  1266 . In the illustrated embodiment, saturation control is performed by applying a global saturation multiplier and a hue-based saturation multiplier for each of the Cb and Cr values. Hue-based saturation control may improve the reproduction of colors. The hue of the color may be represented in the YCbCr color space, as shown by the color wheel graph  1270  in  FIG. 97 . As will be appreciated, the YCbCr hue and saturation color wheel  1270  may be derived by shifting the identical color wheel in the HSV color space (hue, saturation, and intensity) by approximately 109 degrees. As shown, the graph  1270  includes circumferential values representing the saturation multiplier (S) within a range of 0 to 1, as well as angular values representing θ, as defined above, within a range of between 0 to 360°. Each θ may represent a different color (e.g., 49°=magenta, 109°=red, 229°=green, etc.). The hue of the color at a particular hue angle θ may be adjusted by selecting an appropriate saturation multiplier S. 
     Referring back to  FIG. 96 , the hue angle θ (calculated in the global hue control block  1264 ) may be used as an index for a Cb saturation lookup table  1268  and a Cr saturation lookup table  1269 . In one embodiment, the saturation lookup tables  1268  and  1269  may contain 256 saturation values distributed evenly in the hue range from 0-360° (e.g., the first lookup table entry is at 0° and the last entry is at 360°) and the saturation value S at a given pixel may be determined via linear interpolation of saturation values in the lookup table just below and above the current hue angle θ. A final saturation value for each of the Cb and Cr components is obtained by multiplying a global saturation value (which may be a global constant for each of Cb and Cr) with the determined hue-based saturation value. Thus, the final corrected Cb′ and Cr′ values may be determined by multiplying Cb adj  and Cr adj  with their respective final saturation values, as shown in the hue-based saturation control block  1266 . 
     Thereafter, the output of the BCC logic  1172  is passed to the YCbCr gamma adjustment logic  1174 , as shown in  FIG. 90 . In one embodiment, the gamma adjustment logic  1174  may provide non-linear mapping functions for the Y, Cb and Cr channels. For instance, the input Y, Cb, and Cr values are mapped to corresponding output values. Again, assuming that the YCbCr data is processed in 10-bits, an interpolated 10-bit 256 entry lookup table may be utilized. Three such lookup tables may be provided with one for each of the Y, Cb, and Cr channels. Each of the 256 input entries may be evenly distributed and, an output may be determined by linear interpolation of the output values mapped to the indices just above and below the current input index. In some embodiments, a non-interpolated lookup table having 1024 entries (for 10-bit data) may also be used, but may have significantly greater memory requirements. As will be appreciated, by adjusting the output values of the lookup tables, the YCbCr gamma adjustment function may be also be used to perform certain image filter effects, such as black and white, sepia tone, negative images, solarization, and so forth. 
     Next, chroma decimation may be applied by the chroma decimation logic  1176  to the output of the gamma adjustment logic  1174 . In one embodiment, the chroma decimation logic  1176  may be configured to perform horizontal decimation to convert the YCbCr data from a 4:4:4 format to a 4:2:2 format, in which the chroma (Cr and Cr) information is sub-sampled at half rate of the luma data. By way of example only, decimation may be performed by applying a 7-tap low pass filter, such as a half-band lanczos filter, to a set of 7 horizontal pixels, as shown below: 
                     Out   =               C   ⁢           ⁢   0   ×     in   ⁡     (     i   -   3     )         +     C   ⁢           ⁢   1   ×     in   ⁡     (     i   -   2     )         +     C   ⁢           ⁢   2   ×     in   ⁡     (     i   -   1     )         +     C   ⁢           ⁢   3   ×                   in   ⁡     (   i   )       +     C   ⁢           ⁢   4   ×     in   ⁡     (     i   +   1     )         +     C   ⁢           ⁢   5   ×     in   ⁡     (     i   +   2     )         +     C   ⁢           ⁢   6   ×     in   ⁡     (     i   +   3     )                 512       ,           (   112   )               
wherein in(i) represents the input pixel (Cb or Cr), and C 0 -C 6  represent the filtering coefficients of the 7-tap filter. Each input pixel has an independent filter coefficient (C 0 -C 6 ) to allow flexible phase offset for the chroma filtered samples.
 
     Further, chroma decimation may, in some instances, also be performed without filtering. This may be useful when the source image was originally received in 4:2:2 format, but was up-sampled to 4:4:4 format for YCbCr processing. In this case, the resulting decimated 4:2:2 image is identical to the original image. 
     Subsequently, the YCbCr data output from the chroma decimation logic  1176  may be scaled using the scaling logic  1178  prior to being output from the YCbCr processing block  904 . The function of the scaling logic  1178  may be similar to the functionality of the scaling logic  368 ,  370  in the binning compensation filter  300  of the front-end pixel processing unit  130 , as discussed above with reference to  FIG. 28 . For instance, the scaling logic  1178  may perform horizontal and vertical scaling as two steps. In one embodiment, a 5-tap polyphase filter may be used for vertical scaling, and a 9-tap polyphase filter may be used for horizontal scaling. The multi-tap polyphase filters may multiply pixels selected from the source image by a weighting factor (e.g., filter coefficient), and then sum the outputs to form the destination pixel. The selected pixels may be chosen depending on the current pixel position and the number of filters taps. For instance, with a vertical 5-tap filter, two neighboring pixels on each vertical side of a current pixel may be selected and, with a horizontal 9-tap filter, four neighboring pixels on each horizontal side of the current pixel may be selected. The filtering coefficients may be provided from a lookup table, and may be determined by the current between-pixel fractional position. The output  926  of the scaling logic  1178  is then output from the YCbCr processing block  904 . 
     Returning back to  FIG. 67 , the processed output signal  926  may be sent to the memory  108 , or may be output from the ISP pipe processing logic  82  as the image signal  114  to display hardware (e.g., display  28 ) for viewing by a user, or to a compression engine (e.g., encoder  118 ). In some embodiments, the image signal  114  may be further processed by a graphics processing unit and/or a compression engine and stored before being decompressed and provided to a display. Additionally, one or more frame buffers may also be provided to control the buffering of the image data being output to a display, particularly with respect to video image data. 
     As will be understood, the various image processing techniques described above and relating to defective pixel detection and correction, lens shading correction, demosaicing, and image sharpening, among others, are provided herein by way of example only. Accordingly, it should be understood that the present disclosure should not be construed as being limited to only the examples provided above. Indeed, the exemplary logic depicted herein may be subject to a number of variations and/or additional features in other embodiments. Further, it should be appreciated that the above-discussed techniques may be implemented in any suitable manner. For instance, the components of the image processing circuitry  32 , and particularly the ISP front-end block  80  and the ISP pipe block  82  may be implemented using hardware (e.g., suitably configured circuitry), software (e.g., via a computer program including executable code stored on one or more tangible computer readable medium), or via using a combination of both hardware and software elements. 
     The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.