PATENT DOCUMENT

Publication Number: US-11074678-B2
Application Number: US-201916393892-A
Country: US
Kind Code: B2

Title: Biasing a noise filter to preserve image texture

Abstract:
Embodiments relate to biasing an image noise filter to reduce edge and texture blurring of image data. Pixel values used to determine photometric coefficients for a bilateral filter are modified by offset values. The offset value for a pixel value is determined by applying a high pass filter to the pixel (referred to as the center pixel) and neighboring pixels of the center pixel. By adding the offset value to the center pixel value, the pixel value difference between the neighboring pixels and the center pixel becomes smaller for pixels on the same side of an edge as the center pixel. Thus, pixels on the same side of the edge get more weight in the bilateral noise filter. Conversely, pixels on the opposite side of the edge as the center pixel get less weight in the bilateral filter. As a result, the biased bilateral filter reduces blurring of edges and increases preservation of texture in the image data.

Claims:
The invention claimed is: 
     
       1. An image processor comprising:
 an offset calculator circuit configured to:
 receive pixel values for a block of original pixels in image data, the block of original pixels comprising a center pixel and neighboring pixels within a predetermined distance from the center pixel, and 
 apply a high pass filter to pixel values of the block of original pixels to generate an offset value for the center pixel; 
 
 a center pixel modifier circuit coupled to the offset calculator circuit and configured to adjust a pixel value of the center pixel by the offset value to generate a modified pixel value for a modified pixel that becomes part of a modified pixel block, the modified pixel block comprising the modified pixel value and the neighboring pixel values; and 
 a photometric distance calculator circuit coupled to a pixel block assembler circuit to receive the modified pixel block, the photometric distance calculator circuit configured to determine photometric distances of pixel values in the modified pixel block and determine photometric coefficients by processing the photometric distances for generating a bilateral filter that preserves texture in the image data. 
 
     
     
       2. The image processor of  claim 1 , further comprising:
 the pixel block assembler circuit coupled to the center pixel modifier circuit to receive the modified pixel value, the pixel block assembler circuit configured to assemble the modified pixel block by including at least the modified pixel value as a pixel value for one of the pixels in the block of modified pixels. 
 
     
     
       3. The image processor of  claim 2 , wherein the image processor further comprises a bilateral filter circuit coupled to the photometric distance calculator circuit and configured to perform bilateral filtering by at least multiplying one of the photometric coefficients, a corresponding spatial coefficient of the block of original pixels, and a pixel value of a corresponding pixel in the block of original pixels. 
     
     
       4. The image processor of  claim 1 , wherein the offset calculator circuit comprises a clipping circuit configured to set the offset value as a first value if the offset value is above the first value and set the offset value as a second value if the offset value is below the second value. 
     
     
       5. The image processor of  claim 4 , wherein the first value and the second value are programmed into a look up table. 
     
     
       6. The image processor of  claim 1 , wherein the block of original pixels is a 5×5 block of pixels. 
     
     
       7. The image processor of  claim 1 , wherein the high pass filter is a Laplacian high pass filter. 
     
     
       8. The image processor of  claim 1 , wherein the high pass filter is a Laplacian of Gaussian high pass filter. 
     
     
       9. The image processor of  claim 1 , wherein the image data is luminance image data. 
     
     
       10. The image processor of  claim 1 , wherein the image data is one of red, blue, or green image data. 
     
     
       11. A method comprising:
 receiving, by an offset calculator circuit, pixel values for a block of original pixels in image data, the block of original pixels comprising a center pixel and neighboring pixels within a predetermined distance from the center pixel; 
 applying, by the offset calculator circuit, a high pass filter to pixel values of the block of original pixels to generate an offset value for the center pixel; 
 adjusting, by a center pixel modifier circuit coupled to the offset calculator, a pixel value of the center pixel by the offset value to generate a modified pixel value for a modified pixel that becomes part of a modified pixel block, the modified pixel block comprising the modified pixel value and the neighboring pixel values; 
 determining, by a photometric distance calculator circuit, photometric distances of pixel values in the modified pixel block; and 
 determining, by the photometric distance calculator circuit, photometric coefficients by processing the photometric distances. 
 
     
     
       12. The method of  claim 11 , further comprising:
 assembling the block of modified pixels by including at least the modified pixel value as a pixel value for one of pixels in the block of modified pixels. 
 
     
     
       13. The method of  claim 12  further comprising:
 performing, by a bilateral filter circuit coupled to the photometric distance calculator circuit, bilateral filtering by at least multiplying one of the photometric coefficients, a corresponding spatial coefficient of the block of original pixels, and a pixel value of a corresponding pixel in the block of original pixels. 
 
     
     
       14. The method of  claim 11 , wherein the offset calculator circuit comprises a clipping circuit configured to set the offset value as a first value if the offset value is above the first value and set the offset value as a second value if the offset value is below the second value. 
     
     
       15. The method of  claim 14 , wherein the high pass filer is a Laplacian high pass filter or a Laplacian of Gaussian high pass filter. 
     
     
       16. A system comprising:
 an image sensor configured to capture image data; 
 an image processor comprising:
 an offset calculator circuit configured to:
 receive pixel values for a block of original pixels in a version of the captured image data, the block of original pixels comprising a center pixel and neighboring pixels within a predetermined distance from the center pixel, and 
 apply a high pass filter to pixel values of the block of original pixels to generate an offset value for the center pixel; 
 
 a center pixel modifier circuit coupled to the offset calculator and configured to adjust a pixel value of the center pixel by the offset value to generate a modified pixel value for a modified pixel that becomes part of a modified pixel block, the modified pixel block comprising the modified pixel value and the neighboring pixel values; and 
 a photometric distance calculator circuit coupled to a pixel block assembler circuit to receive the modified pixel block, the photometric distance calculator configured to determine photometric distances of pixel values in the modified pixel block and determine photometric coefficients by processing the photometric distances. 
 
 
     
     
       17. The system of  claim 16 , wherein the image processor further comprises:
 the pixel block assembler circuit coupled to the center pixel modifier to receive the modified pixel value, the pixel block assembler circuit configured to assemble the block of modified pixels by including at least the modified pixel value as a pixel value for one of the pixels in the block of modified pixels. 
 
     
     
       18. The system of  claim 17 , wherein the image processor further comprises:
 a bilateral filter circuit coupled to the photometric distance calculator circuit and configured to perform bilateral filtering by at least multiplying one of the photometric coefficients, a corresponding spatial coefficient of the block of original pixels, and a pixel value of a corresponding pixel in the block of original pixels. 
 
     
     
       19. The system of  claim 16 , wherein the offset calculator circuit comprises a clipping circuit configured to set the offset value as a first value if the offset value is above the first value and set the offset value as a second value if the offset value is below the second value. 
     
     
       20. The system of  claim 16 , wherein the high pass filter is a Laplacian high pass filter or a Laplacian of Gaussian high pass filter.

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates a circuit for processing images and more specifically to biasing an image noise filter to reduce edge and texture blurring of image data. 
     2. Description of the Related Arts 
     Image data captured by an image sensor or received from other data sources is often processed in an image processing pipeline before further processing or consumption. For example, raw image data may be corrected, filtered, or otherwise modified before being provided to subsequent components such as a video encoder. To perform corrections or enhancements for captured image data, various components, unit stages or modules may be employed. 
     Such an image processing pipeline may be structured so that corrections or enhancements to the captured image data can be performed in an expedient way without consuming other system resources. Although many image processing algorithms may be performed by executing software programs on central processing unit (CPU), execution of such programs on the CPU would consume significant bandwidth of the CPU and other peripheral resources as well as increase power consumption. Hence, image processing pipelines are often implemented as a hardware component separate from the CPU and dedicated to performing one or more image processing algorithms. 
     Image processing pipelines often include filters to reduce noise in image data. However, noise filters often blur edges and texture, thus degrading the overall quality of the image data. 
     SUMMARY 
     Embodiments relate to an image processor that includes an offset calculator circuit and a center pixel modifier circuit that is coupled to the offset calculator circuit. The offset calculator circuit receives pixel values for a block of original pixels in an image data. The block of original pixels includes a center pixel and neighboring pixels within a predetermined distance from the center pixel. The offset calculator circuit applies a high pass filter to pixel values of the block of original pixels to generate an offset value for the center pixel. The center pixel modifier circuit adjusts a pixel value of the center pixel by the offset value to generate a modified pixel value for a modified pixel. The modified pixel becomes part of a modified pixel block that includes the modified pixel value and the neighboring pixels. The modified pixel block is processed to determine photometric distances of a bilateral filter that preserves texture in the image data. 
     In some embodiments, the image processor also includes a pixel block assembler circuit and a photometric distance calculator circuit. The pixel block assembler circuit is coupled to the center pixel modifier circuit and the photometric distance calculator circuit is coupled to the pixel block assembler circuit. The pixel block assembler circuit assembles the modified pixel block by including at least the modified pixel value as a pixel value for one of the pixels in the block of modified pixels. The photometric distance calculator circuit receives the modified pixel block and determines photometric distances of pixel values in the modified pixel block. The photometric distance calculator circuit also determines photometric coefficients by processing the photometric distances. In some embodiments, the image processor also includes a bilateral filter circuit coupled to the photometric distance calculator circuit. The bilateral filter circuit performs bilateral filtering by at least multiplying one of the photometric coefficients derived from the photometric distances, a corresponding spatial coefficient of the block of original pixels, and a pixel value of a corresponding pixel in the block of original pixels. 
     In some embodiments, the offset calculator circuit includes a clipping circuit configured to set the offset value as a first value if the offset value is above the first value and set the offset value as a second value if the offset value is below the second value. In some embodiments, the first value and the second value are programmed into a look up table. 
     In some embodiments, the block of original pixels is a 5×5 block of pixels. 
     In some embodiments, the high pass filter is a Laplacian high pass filter. 
     In some embodiments, the high pass filter is a Laplacian of Gaussian high pass filter. 
     In some embodiments, the image data is luminance image data. 
     In some embodiments, the image data is one of red, blue, or green image data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Figure ( FIG. 1  is a high-level diagram of an electronic device, according to one embodiment 
         FIG. 2  is a block diagram illustrating components in the electronic device, according to one embodiment. 
         FIG. 3  is a block diagram illustrating image processing pipelines implemented using an image signal processor, according to one embodiment. 
         FIG. 4  is a block diagram illustrating a portion of the image processing pipeline including a multiple band noise reduction circuit, according to one embodiment. 
         FIG. 5  is a block diagram illustrating a multiple band noise reduction circuit, according to one embodiment. 
         FIG. 6  is a block diagram illustrating components of a texture preservation circuit, according to one embodiment. 
         FIG. 7  is a flowchart illustrating a method of biasing an image noise filter to reduce edge and texture blurring of image data, according to one embodiment. 
     
    
    
     The figures depict, and the detail description describes, various non-limiting embodiments for purposes of illustration only. 
     DETAILED DESCRIPTION 
     Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, the described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments. 
     Embodiments of the present disclosure relate to biasing a noise filter to reduce edge and texture blurring of image data. In bilateral filtering, photometric coefficients of a photometric kernel (also referred to as range kernel) and spatial coefficients of a spatial kernel are multiplied by pixel values from a set of image data. The noise filter modifies pixel values by offset values to determine photometric coefficients of a bilateral noise filter. The offset value is obtained by applying a high pass filter to a block of pixels including the pixel for modification as the center pixel. The offset value may be positive if the center pixel of the block is on the brighter side of an edge. The offset value may be negative if the center pixel is on the darker side of the edge. By adding the offset value to the center pixel value, the pixel value difference between neighboring pixels and the center pixel becomes smaller for pixels on the same side of the edge as the center pixel. Thus, pixels on the same side of the edge get more weight in the bilateral noise filter. Conversely, pixels on the opposite side of the edge as the center pixel get less weight in the bilateral filter. As a result, the biased bilateral filter reduces blurring of edges and increases preservation of texture in the image data. 
     Exemplary Electronic Device 
     Embodiments of electronic devices, user interfaces for such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as personal digital assistant (PDA) and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, Apple Watch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable electronic devices, such as wearables, laptops or tablet computers, are optionally used. In some embodiments, the device is not a portable communications device, but is a desktop computer or other computing device that is not designed for portable use. In some embodiments, the disclosed electronic device may include a touch sensitive surface (e.g., a touch screen display and/or a touch pad). An example electronic device described below in conjunction with  FIG. 1  (e.g., device  100 ) may include a touch-sensitive surface for receiving user input. The electronic device may also include one or more other physical user-interface devices, such as a physical keyboard, a mouse and/or a joystick. 
     Figure ( FIG. 1  is a high-level diagram of an electronic device  100 , according to one embodiment. Device  100  may include one or more physical buttons, such as a “home” or menu button  104 . Menu button  104  is, for example, used to navigate to any application in a set of applications that are executed on device  100 . In some embodiments, menu button  104  includes a fingerprint sensor that identifies a fingerprint on menu button  104 . The fingerprint sensor may be used to determine whether a finger on menu button  104  has a fingerprint that matches a fingerprint stored for unlocking device  100 . Alternatively, in some embodiments, menu button  104  is implemented as a soft key in a graphical user interface (GUI) displayed on a touch screen. 
     In some embodiments, device  100  includes touch screen  150 , menu button  104 , push button  106  for powering the device on/off and locking the device, volume adjustment buttons  108 , Subscriber Identity Module (SIM) card slot  110 , head set jack  112 , and docking/charging external port  124 . Push button  106  may be used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  100  also accepts verbal input for activation or deactivation of some functions through microphone  113 . The device  100  includes various components including, but not limited to, a memory (which may include one or more computer readable storage mediums), a memory controller, one or more central processing units (CPUs), a peripherals interface, an RF circuitry, an audio circuitry, speaker  111 , microphone  113 , input/output (I/O) subsystem, and other input or control devices. Device  100  may include one or more image sensors  164 , one or more proximity sensors  166 , and one or more accelerometers  168 . Device  100  may include more than one type of image sensors  164 . Each type may include more than one image sensor  164 . For example, one type of image sensors  164  may be cameras and another type of image sensors  164  may be infrared sensors that may be used for face recognition. Additionally or alternatively, the image sensors  164  may be associated with different lens configuration. For example, device  100  may include rear image sensors, one with a wide-angle lens and another with as a telephoto lens. The device  100  may include components not shown in  FIG. 1  such as an ambient light sensor, a dot projector and a flood illuminator. 
     Device  100  is only one example of an electronic device, and device  100  may have more or fewer components than listed above, some of which may be combined into a component or have a different configuration or arrangement. The various components of device  100  listed above are embodied in hardware, software, firmware or a combination thereof, including one or more signal processing and/or application specific integrated circuits (ASICs). While the components in  FIG. 1  are shown as generally located on the same side as the touch screen  150 , one or more components may also be located on an opposite side of device  100 . For example, the front side of device  100  may include an infrared image sensor  164  for face recognition and another image sensor  164  as the front camera of device  100 . The back side of device  100  may also include additional two image sensors  164  as the rear cameras of device  100 . 
       FIG. 2  is a block diagram illustrating components in device  100 , according to one embodiment. Device  100  may perform various operations including image processing. For this and other purposes, the device  100  may include, among other components, image sensor  202 , system-on-a chip (SOC) component  204 , system memory  230 , persistent storage (e.g., flash memory)  228 , orientation sensor  234 , and display  216 . The components as illustrated in  FIG. 2  are merely illustrative. For example, device  100  may include other components (such as speaker or microphone) that are not illustrated in  FIG. 2 . Further, some components (such as orientation sensor  234 ) may be omitted from device  100 . 
     Image sensors  202  are components for capturing image data. Each of the image sensors  202  may be embodied, for example, as a complementary metal-oxide-semiconductor (CMOS) active-pixel sensor, a camera, video camera, or other devices. Image sensors  202  generate raw image data that is sent to SOC component  204  for further processing. In some embodiments, the image data processed by SOC component  204  is displayed on display  216 , stored in system memory  230 , persistent storage  228  or sent to a remote computing device via network connection. The raw image data generated by image sensors  202  may be in a Bayer color filter array (CFA) pattern (hereinafter also referred to as “Bayer pattern”). An image sensor  202  may also include optical and mechanical components that assist image sensing components (e.g., pixels) to capture images. The optical and mechanical components may include an aperture, a lens system, and an actuator that controls the focal length of the image sensor  202 . 
     Motion sensor  234  is a component or a set of components for sensing motion of device  100 . Motion sensor  234  may generate sensor signals indicative of orientation and/or acceleration of device  100 . The sensor signals are sent to SOC component  204  for various operations such as turning on device  100  or rotating images displayed on display  216 . 
     Display  216  is a component for displaying images as generated by SOC component  204 . Display  216  may include, for example, liquid crystal display (LCD) device or an organic light emitting diode (OLED) device. Based on data received from SOC component  204 , display  116  may display various images, such as menus, selected operating parameters, images captured by image sensor  202  and processed by SOC component  204 , and/or other information received from a user interface of device  100  (not shown). 
     System memory  230  is a component for storing instructions for execution by SOC component  204  and for storing data processed by SOC component  204 . System memory  230  may be embodied as any type of memory including, for example, dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate (DDR, DDR2, DDR3, etc.) RAMBUS DRAM (RDRAM), static RAM (SRAM) or a combination thereof. In some embodiments, system memory  230  may store pixel data or other image data or statistics in various formats. 
     Persistent storage  228  is a component for storing data in a non-volatile manner. Persistent storage  228  retains data even when power is not available. Persistent storage  228  may be embodied as read-only memory (ROM), flash memory or other non-volatile random access memory devices. 
     SOC component  204  is embodied as one or more integrated circuit (IC) chip and performs various data processing processes. SOC component  204  may include, among other subcomponents, image signal processor (ISP)  206 , a central processor unit (CPU)  208 , a network interface  210 , motion sensor interface  212 , display controller  214 , graphics processor (GPU)  220 , memory controller  222 , video encoder  224 , storage controller  226 , and various other input/output (I/O) interfaces  218 , and bus  232  connecting these subcomponents. SOC component  204  may include more or fewer subcomponents than those shown in  FIG. 2 . 
     ISP  206  is hardware that performs various stages of an image processing pipeline. In some embodiments, ISP  206  may receive raw image data from image sensor  202 , and process the raw image data into a form that is usable by other subcomponents of SOC component  204  or components of device  100 . ISP  206  may perform various image-manipulation operations such as image translation operations, horizontal and vertical scaling, color space conversion and/or image stabilization transformations, as described below in detail with reference to  FIG. 3 . 
     CPU  208  may be embodied using any suitable instruction set architecture, and may be configured to execute instructions defined in that instruction set architecture. CPU  208  may be general-purpose or embedded processors using any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, RISC, ARM or MIPS ISAs, or any other suitable ISA. Although a single CPU is illustrated in  FIG. 2 , SOC component  204  may include multiple CPUs. In multiprocessor systems, each of the CPUs may commonly, but not necessarily, implement the same ISA. 
     Graphics processing unit (GPU)  220  is graphics processing circuitry for performing operations on graphical data. For example, GPU  220  may render objects to be displayed into a frame buffer (e.g., one that includes pixel data for an entire frame). GPU  220  may include one or more graphics processors that may execute graphics software to perform a part or all of the graphics operation, or hardware acceleration of certain graphics operations. 
     I/O interfaces  218  are hardware, software, firmware or combinations thereof for interfacing with various input/output components in device  100 . I/O components may include devices such as keypads, buttons, audio devices, and sensors such as a global positioning system. I/O interfaces  218  process data for sending data to such I/O components or process data received from such I/O components. 
     Network interface  210  is a subcomponent that enables data to be exchanged between devices  100  and other devices via one or more networks (e.g., carrier or agent devices). For example, video or other image data may be received from other devices via network interface  210  and be stored in system memory  230  for subsequent processing (e.g., via a back-end interface to image signal processor  206 , such as discussed below in  FIG. 3 ) and display. The networks may include, but are not limited to, Local Area Networks (LANs) (e.g., an Ethernet or corporate network) and Wide Area Networks (WANs). The image data received via network interface  210  may undergo image processing processes by ISP  206 . 
     Motion sensor interface  212  is circuitry for interfacing with motion sensor  234 . Motion sensor interface  212  receives sensor information from motion sensor  234  and processes the sensor information to determine the orientation or movement of the device  100 . 
     Display controller  214  is circuitry for sending image data to be displayed on display  216 . Display controller  214  receives the image data from ISP  206 , CPU  208 , graphic processor or system memory  230  and processes the image data into a format suitable for display on display  216 . 
     Memory controller  222  is circuitry for communicating with system memory  230 . Memory controller  222  may read data from system memory  230  for processing by ISP  206 , CPU  208 , GPU  220  or other subcomponents of SOC component  204 . Memory controller  222  may also write data to system memory  230  received from various subcomponents of SOC component  204 . 
     Video encoder  224  is hardware, software, firmware or a combination thereof for encoding video data into a format suitable for storing in persistent storage  128  or for passing the data to network interface w 10  for transmission over a network to another device. 
     In some embodiments, one or more subcomponents of SOC component  204  or some functionality of these subcomponents may be performed by software components executed on ISP  206 , CPU  208  or GPU  220 . Such software components may be stored in system memory  230 , persistent storage  228  or another device communicating with device  100  via network interface  210 . 
     Image data or video data may flow through various data paths within SOC component  204 . In one example, raw image data may be generated from the image sensors  202  and processed by ISP  206 , and then sent to system memory  230  via bus  232  and memory controller  222 . After the image data is stored in system memory  230 , it may be accessed by video encoder  224  for encoding or by display  116  for displaying via bus  232 . 
     In another example, image data is received from sources other than the image sensors  202 . For example, video data may be streamed, downloaded, or otherwise communicated to the SOC component  204  via wired or wireless network. The image data may be received via network interface  210  and written to system memory  230  via memory controller  222 . The image data may then be obtained by ISP  206  from system memory  230  and processed through one or more image processing pipeline stages, as described below in detail with reference to  FIG. 3 . The image data may then be returned to system memory  230  or be sent to video encoder  224 , display controller  214  (for display on display  216 ), or storage controller  226  for storage at persistent storage  228 . 
     Example Image Signal Processing Pipelines 
       FIG. 3  is a block diagram illustrating image processing pipelines implemented using ISP  206 , according to one embodiment. In the embodiment of  FIG. 3 , ISP  206  is coupled to an image sensor system  201  that includes one or more image sensors  202 A through  202 N (hereinafter collectively referred to as “image sensors  202 ” or also referred individually as “image sensor  202 ”) to receive raw image data. The image sensor system  201  may include one or more sub-systems that control the image sensors  202  individually. In some cases, each image sensor  202  may operate independently while, in other cases, the image sensors  202  may share some components. For example, in one embodiment, two or more image sensors  202  may be share the same circuit board that controls the mechanical components of the image sensors (e.g., actuators that change the focal lengths of each image sensor). The image sensing components of an image sensor  202  may include different types of image sensing components that may provide raw image data in different forms to the ISP  206 . For example, in one embodiment, the image sensing components may include a plurality of focus pixels that are used for auto-focusing and a plurality of image pixels that are used for capturing images. In another embodiment, the image sensing pixels may be used for both auto-focusing and image capturing purposes. 
     ISP  206  implements an image processing pipeline which may include a set of stages that process image information from creation, capture or receipt to output. ISP  206  may include, among other components, sensor interface  302 , central control  320 , front-end pipeline stages  330 , back-end pipeline stages  340 , image statistics module  304 , vision module  322 , back-end interface  342 , output interface  316 , and auto-focus circuits  350 A through  350 N (hereinafter collectively referred to as “auto-focus circuits  350 ” or referred individually as “auto-focus circuits  350 ”). ISP  206  may include other components not illustrated in  FIG. 3  or may omit one or more components illustrated in  FIG. 3 . 
     In one or more embodiments, different components of ISP  206  process image data at different rates. In the embodiment of  FIG. 3 , front-end pipeline stages  330  (e.g., raw processing stage  306  and resample processing stage  308 ) may process image data at an initial rate. Thus, the various different techniques, adjustments, modifications, or other processing operations performed by these front-end pipeline stages  330  at the initial rate. For example, if the front-end pipeline stages  330  process 2 pixels per clock cycle, then raw processing stage  306  operations (e.g., black level compensation, highlight recovery and defective pixel correction) may process 2 pixels of image data at a time. In contrast, one or more back-end pipeline stages  340  may process image data at a different rate less than the initial data rate. For example, in the embodiment of  FIG. 3 , back-end pipeline stages  340  (e.g., noise processing stage  310 , color processing stage  312 , and output rescale  314 ) may be processed at a reduced rate (e.g., 1 pixel per clock cycle). 
     Raw image data captured by image sensors  202  may be transmitted to different components of ISP  206  in different manners. In one embodiment, raw image data corresponding to the focus pixels may be sent to the auto-focus circuits  350  while raw image data corresponding to the image pixels may be sent to the sensor interface  302 . In another embodiment, raw image data corresponding to both types of pixels may simultaneously be sent to both the auto-focus circuits  350  and the sensor interface  302 . 
     Auto-focus circuits  350  may include hardware circuit that analyzes raw image data to determine an appropriate focal length of each image sensor  202 . In one embodiment, the raw image data may include data that is transmitted from image sensing pixels that specializes in image focusing. In another embodiment, raw image data from image capture pixels may also be used for auto-focusing purpose. An auto-focus circuit  350  may perform various image processing operations to generate data that determines the appropriate focal length. The image processing operations may include cropping, binning, image compensation, scaling to generate data that is used for auto-focusing purpose. The auto-focusing data generated by auto-focus circuits  350  may be fed back to the image sensor system  201  to control the focal lengths of the image sensors  202 . For example, an image sensor  202  may include a control circuit that analyzes the auto-focusing data to determine a command signal that is sent to an actuator associated with the lens system of the image sensor to change the focal length of the image sensor. The data generated by the auto-focus circuits  350  may also be sent to other components of the ISP  206  for other image processing purposes. For example, some of the data may be sent to image statistics  304  to determine information regarding auto-exposure. 
     The auto-focus circuits  350  may be individual circuits that are separate from other components such as image statistics  304 , sensor interface  302 , front-end  330  and back-end  340 . This allows the ISP  206  to perform auto-focusing analysis independent of other image processing pipelines. For example, the ISP  206  may analyze raw image data from the image sensor  202 A to adjust the focal length of image sensor  202 A using the auto-focus circuit  350 A while performing downstream image processing of the image data from image sensor  202 B simultaneously. In one embodiment, the number of auto-focus circuits  350  may correspond to the number of image sensors  202 . In other words, each image sensor  202  may have a corresponding auto-focus circuit that is dedicated to the auto-focusing of the image sensor  202 . The device  100  may perform auto focusing for different image sensors  202  even if one or more image sensors  202  are not in active use. This allows a seamless transition between two image sensors  202  when the device  100  switches from one image sensor  202  to another. For example, in one embodiment, a device  100  may include a wide-angle camera and a telephoto camera as a dual back camera system for photo and image processing. The device  100  may display images captured by one of the dual cameras and may switch between the two cameras from time to time. The displayed images may seamless transition from image data captured by one image sensor  202  to image data captured by another image sensor without waiting for the second image sensor  202  to adjust its focal length because two or more auto-focus circuits  350  may continuously provide auto-focus data to the image sensor system  201 . 
     Raw image data captured by different image sensors  202  may also be transmitted to sensor interface  302 . Sensor interface  302  receives raw image data from image sensor  202  and processes the raw image data into an image data processable by other stages in the pipeline. Sensor interface  302  may perform various preprocessing operations, such as image cropping, binning or scaling to reduce image data size. In some embodiments, pixels are sent from the image sensor  202  to sensor interface  302  in raster order (e.g., horizontally, line by line). The subsequent processes in the pipeline may also be performed in raster order and the result may also be output in raster order. Although only a single image sensor and a single sensor interface  302  are illustrated in  FIG. 3 , when more than one image sensor is provided in device  100 , a corresponding number of sensor interfaces may be provided in ISP  206  to process raw image data from each image sensor. 
     Front-end pipeline stages  330  process image data in raw or full-color domains. Front-end pipeline stages  330  may include, but are not limited to, raw processing stage  306  and resample processing stage  308 . A raw image data may be in Bayer raw format, for example. In Bayer raw image format, pixel data with values specific to a particular color (instead of all colors) is provided in each pixel. In an image capturing sensor, image data is typically provided in a Bayer pattern. Raw processing stage  306  may process image data in a Bayer raw format. 
     The operations performed by raw processing stage  306  include, but are not limited, sensor linearization, black level compensation, fixed pattern noise reduction, defective pixel correction, raw noise filtering, lens shading correction, white balance gain, and highlight recovery. Sensor linearization refers to mapping non-linear image data to linear space for other processing. Black level compensation refers to providing digital gain, offset and clip independently for each color component (e.g., Gr, R, B, Gb) of the image data. Fixed pattern noise reduction refers to removing offset fixed pattern noise and gain fixed pattern noise by subtracting a dark frame from an input image and multiplying different gains to pixels. Defective pixel correction refers to detecting defective pixels, and then replacing defective pixel values. Raw noise filtering refers to reducing noise of image data by averaging neighbor pixels that are similar in brightness. Highlight recovery refers to estimating pixel values for those pixels that are clipped (or nearly clipped) from other channels. Lens shading correction refers to applying a gain per pixel to compensate for a dropoff in intensity roughly proportional to a distance from a lens optical center. White balance gain refers to providing digital gains for white balance, offset and clip independently for all color components (e.g., Gr, R, B, Gb in Bayer format). Components of ISP  206  may convert raw image data into image data in full-color domain, and thus, raw processing stage  306  may process image data in the full-color domain in addition to or instead of raw image data. 
     Resample processing stage  308  performs various operations to convert, resample, or scale image data received from raw processing stage  306 . Operations performed by resample processing stage  308  may include, but not limited to, demosaic operation, per-pixel color correction operation, Gamma mapping operation, color space conversion and downscaling or sub-band splitting. Demosaic operation refers to converting or interpolating missing color samples from raw image data (for example, in a Bayer pattern) to output image data into a full-color domain. Demosaic operation may include low pass directional filtering on the interpolated samples to obtain full-color pixels. Per-pixel color correction operation refers to a process of performing color correction on a per-pixel basis using information about relative noise standard deviations of each color channel to correct color without amplifying noise in the image data. Gamma mapping refers to converting image data from input image data values to output data values to perform gamma correction. For the purpose of Gamma mapping, lookup tables (or other structures that index pixel values to another value) for different color components or channels of each pixel (e.g., a separate lookup table for R, G, and B color components) may be used. Color space conversion refers to converting color space of an input image data into a different format. In one embodiment, the resample processing stage  308  converts RGG format into YCbCr format for further processing. In another embodiment, the resample processing state  308  concerts RBD format into RGB format for further processing. 
     Central control module  320  may control and coordinate overall operation of other components in ISP  206 . Central control module  320  performs operations including, but not limited to, monitoring various operating parameters (e.g., logging clock cycles, memory latency, quality of service, and state information), updating or managing control parameters for other components of ISP  206 , and interfacing with sensor interface  302  to control the starting and stopping of other components of ISP  206 . For example, central control module  320  may update programmable parameters for other components in ISP  206  while the other components are in an idle state. After updating the programmable parameters, central control module  320  may place these components of ISP  206  into a run state to perform one or more operations or tasks. Central control module  320  may also instruct other components of ISP  206  to store image data (e.g., by writing to system memory  230  in  FIG. 2 ) before, during, or after resample processing stage  308 . In this way full-resolution image data in raw or full-color domain format may be stored in addition to or instead of processing the image data output from resample processing stage  308  through backend pipeline stages  340 . 
     Image statistics module  304  performs various operations to collect statistic information associated with the image data. The operations for collecting statistics information may include, but not limited to, sensor linearization, replace patterned defective pixels, sub-sample raw image data, detect and replace non-patterned defective pixels, black level compensation, lens shading correction, and inverse black level compensation. After performing one or more of such operations, statistics information such as  3 A statistics (Auto white balance (AWB), auto exposure (AE), histograms (e.g., 2D color or component) and any other image data information may be collected or tracked. In some embodiments, certain pixels&#39; values, or areas of pixel values may be excluded from collections of certain statistics data when preceding operations identify clipped pixels. Although only a single statistics module  304  is illustrated in  FIG. 3 , multiple image statistics modules may be included in ISP  206 . For example, each image sensor  202  may correspond to an individual image statistics unit  304 . In such embodiments, each statistic module may be programmed by central control module  320  to collect different information for the same or different image data. 
     Vision module  322  performs various operations to facilitate computer vision operations at CPU  208  such as facial detection in image data. The vision module  322  may perform various operations including pre-processing, global tone-mapping and Gamma correction, vision noise filtering, resizing, keypoint detection, generation of histogram-of-orientation gradients (HOG) and normalized cross correlation (NCC). The pre-processing may include subsampling or binning operation and computation of luminance if the input image data is not in YCrCb format. Global mapping and Gamma correction can be performed on the pre-processed data on luminance image. Vision noise filtering is performed to remove pixel defects and reduce noise present in the image data, and thereby, improve the quality and performance of subsequent computer vision algorithms. Such vision noise filtering may include detecting and fixing dots or defective pixels, and performing bilateral filtering to reduce noise by averaging neighbor pixels of similar brightness. Various vision algorithms use images of different sizes and scales. Resizing of an image is performed, for example, by binning or linear interpolation operation. Keypoints are locations within an image that are surrounded by image patches well suited to matching in other images of the same scene or object. Such keypoints are useful in image alignment, computing camera pose and object tracking. Keypoint detection refers to the process of identifying such keypoints in an image. HOG provides descriptions of image patches for tasks in mage analysis and computer vision. HOG can be generated, for example, by (i) computing horizontal and vertical gradients using a simple difference filter, (ii) computing gradient orientations and magnitudes from the horizontal and vertical gradients, and (iii) binning the gradient orientations. NCC is the process of computing spatial cross-correlation between a patch of image and a kernel. 
     Back-end interface  342  receives image data from other image sources than image sensor  102  and forwards it to other components of ISP  206  for processing. For example, image data may be received over a network connection and be stored in system memory  230 . Back-end interface  342  retrieves the image data stored in system memory  230  and provides it to back-end pipeline stages  340  for processing. One of many operations that are performed by back-end interface  342  is converting the retrieved image data to a format that can be utilized by back-end processing stages  340 . For instance, back-end interface  342  may convert RGB, YCbCr 4:2:0, or YCbCr 4:2:2 formatted image data into YCbCr 4:4:4 color format. 
     Back-end pipeline stages  340  processes image data according to a particular full-color format (e.g., YCbCr 4:4:4 or RGB). In some embodiments, components of the back-end pipeline stages  340  may convert image data to a particular full-color format before further processing. Back-end pipeline stages  340  may include, among other stages, noise processing stage  310  and color processing stage  312 . Back-end pipeline stages  340  may include other stages not illustrated in  FIG. 3 . 
     Noise processing stage  310  performs various operations to reduce noise in the image data. The operations performed by noise processing stage  310  include, but are not limited to, color space conversion, gamma/de-gamma mapping, temporal filtering, noise filtering, luma sharpening, and chroma noise reduction. The color space conversion may convert an image data from one color space format to another color space format (e.g., RGB format converted to YCbCr format). Gamma/de-gamma operation converts image data from input image data values to output data values to perform gamma correction or reverse gamma correction. Temporal filtering filters noise using a previously filtered image frame to reduce noise. For example, pixel values of a prior image frame are combined with pixel values of a current image frame. Noise filtering may include, for example, spatial noise filtering. Luma sharpening may sharpen luma values of pixel data while chroma suppression may attenuate chroma to gray (e.g., no color). In some embodiment, the luma sharpening and chroma suppression may be performed simultaneously with spatial nose filtering. The aggressiveness of noise filtering may be determined differently for different regions of an image. Spatial noise filtering may be included as part of a temporal loop implementing temporal filtering. For example, a previous image frame may be processed by a temporal filter and a spatial noise filter before being stored as a reference frame for a next image frame to be processed. In other embodiments, spatial noise filtering may not be included as part of the temporal loop for temporal filtering (e.g., the spatial noise filter may be applied to an image frame after it is stored as a reference image frame and thus the reference frame is not spatially filtered. 
     Color processing stage  312  may perform various operations associated with adjusting color information in the image data. The operations performed in color processing stage  312  include, but are not limited to, local tone mapping, gain/offset/clip, color correction, three-dimensional color lookup, gamma conversion, and color space conversion. Local tone mapping refers to spatially varying local tone curves in order to provide more control when rendering an image. For instance, a two-dimensional grid of tone curves (which may be programmed by the central control module  320 ) may be bi-linearly interpolated such that smoothly varying tone curves are created across an image. In some embodiments, local tone mapping may also apply spatially varying and intensity varying color correction matrices, which may, for example, be used to make skies bluer while turning down blue in the shadows in an image. Digital gain/offset/clip may be provided for each color channel or component of image data. Color correction may apply a color correction transform matrix to image data. 3D color lookup may utilize a three-dimensional array of color component output values (e.g., R, G, B) to perform advanced tone mapping, color space conversions, and other color transforms. Gamma conversion may be performed, for example, by mapping input image data values to output data values in order to perform gamma correction, tone mapping, or histogram matching. Color space conversion may be implemented to convert image data from one color space to another (e.g., RGB to YCbCr). Other processing techniques may also be performed as part of color processing stage  312  to perform other special image effects, including black and white conversion, sepia tone conversion, negative conversion, or solarize conversion. 
     Output rescale module  314  may resample, transform and correct distortion on the fly as the ISP  206  processes image data. Output rescale module  314  may compute a fractional input coordinate for each pixel and uses this fractional coordinate to interpolate an output pixel via a polyphase resampling filter. A fractional input coordinate may be produced from a variety of possible transforms of an output coordinate, such as resizing or cropping an image (e.g., via a simple horizontal and vertical scaling transform), rotating and shearing an image (e.g., via non-separable matrix transforms), perspective warping (e.g., via an additional depth transform) and per-pixel perspective divides applied in piecewise in strips to account for changes in image sensor during image data capture (e.g., due to a rolling shutter), and geometric distortion correction (e.g., via computing a radial distance from the optical center in order to index an interpolated radial gain table, and applying a radial perturbance to a coordinate to account for a radial lens distortion). 
     Output rescale module  314  may apply transforms to image data as it is processed at output rescale module  314 . Output rescale module  314  may include horizontal and vertical scaling components. The vertical portion of the design may implement series of image data line buffers to hold the “support” needed by the vertical filter. As ISP  206  may be a streaming device, it may be that only the lines of image data in a finite-length sliding window of lines are available for the filter to use. Once a line has been discarded to make room for a new incoming line, the line may be unavailable. Output rescale module  314  may statistically monitor computed input Y coordinates over previous lines and use it to compute an optimal set of lines to hold in the vertical support window. For each subsequent line, output rescale module may automatically generate a guess as to the center of the vertical support window. In some embodiments, the output rescale module  314  may implement a table of piecewise perspective transforms encoded as digital difference analyzer (DDA) steppers to perform a per-pixel perspective transformation between an input image data and output image data in order to correct artifacts and motion caused by sensor motion during the capture of the image frame. Output rescale may provide image data via output interface  316  to various other components of device  100 , as discussed above with regard to  FIGS. 1 and 2 . 
     In various embodiments, the functionally of components  302  through  350  may be performed in a different order than the order implied by the order of these functional units in the image processing pipeline illustrated in  FIG. 3 , or may be performed by different functional components than those illustrated in  FIG. 3 . Moreover, the various components as described in  FIG. 3  may be embodied in various combinations of hardware, firmware or software. 
     Example Pipeline Associated with Multiple Band Noise Reduction Circuit 
       FIG. 4  is a block diagram illustrating a portion of the image processing pipeline including a multiple band noise reduction (MBNR) circuit  420 , according to one embodiment. In the embodiment of  FIG. 4 , MBNR circuit  420  is part of a resample processing stage  308  that also includes, among other components, a scaler  410  and a sub-band splitter circuit  430 . The resample processing stage  308  performs scaling, noise reduction, and sub-band splitting in a recursive manner. 
     As a result of recursive processing, the resample processing stage  380  outputs a series of high frequency component image data HF(N) and low frequency component image data LF(N) derived from an original input image  402  where N represents the levels of downsampling performed on the original input image  402 . For example, HF(0) and LF(0) represent a high frequency component image data and a low frequency component image data split from the original input image  402 , respectively, while HF(1) and LF(1) represent a high frequency component image data and a low frequency component image data split from a first downscaled version of the input image  402 , respectively. 
     MBNR circuit  420  is a circuit that performs noise reduction on multiple bands of the input image  402 . The input image  402  is first passed on through a multiplexer  414  to MBNR circuit  420  for noise reduction. The noise reduced version  422  of the original input image  402  is generated by MBNR circuit  420  and fed to a sub-band splitter  430 . The sub-band splitter  430  splits the noise reduced  422  version of the original input image  402  into the high frequency component image data HF(0) and the low frequency component image data LF(0). The high frequency component image data HF(0) is passed onto a sub-band processing pipeline  448  and then to a sub-band merger  352 . In contrast, the low frequency component image LF(0) is passed through a demultiplexer  440  and is fed back to the resample processing stage  308  for downscaling by a scaler  410 . 
     The scaler  410  generates a downscaled version  412  of the low frequency component image LF(0) fed to the scaler  410 , and passes it onto MBNR circuit  420  via the multiplexer  414  for noise reduction. MBNR circuit  420  performs noise reduction to generate a noise reduced version  432  of the downscaled image  412  and sends it to the sub-band splitter  430  to again split the processed low frequency image data LF(0) into the high frequency component image data HF(1) and the low frequency component image data LF(1). The high frequency component image data HF(1) is sent to the sub-band processing pipeline  448  and then the sub-band merger  352  whereas the low frequency component image data LF(1) is again fed back to the scaler  410  to repeat the process within the resample processing stage  308 . The process of generating a high frequency component image data HF(N) and a low frequency component image data LF(N) is repeated until the final level of band-splitting is performed by the sub-band splitter  430 . When the final level of band-splitting is reached, the low frequency component image data LF(N) is passed through the demultiplexer  440  and a multiplexer  446  to the sub-band processing pipeline  448  and the sub-band merger  352 . 
     As described above, MBNR circuit  420  performs noise reduction on the input image  402  as well as its downscaled low frequency versions of the input image  402 . This enables MBNR circuit  420  to perform noise reduction on multiple bands of the original input image  402 . It is to be noted, however, that only a single pass of noise reduction may be performed on the input image  402  by MBNR circuit  420  without sub-band splitting and scaling. 
     The sub-band merger  352  merges processed high frequency component image data HF(N)′ and processed low frequency component image data LF(N)′ to generate a processed LF(N−1)′. The processed LF(N−1)′ is then fed back to the sub-band merger  352  via the demultiplexer  450  and the multiplexer  446  for merging with the processed HF (N−1)′ to generate a processed LF(N−2)′. The process of combining the processed high frequency component image data and the processed low frequency component data is repeated until the sub-band merger  352  generates a processed version  454  of input image that is outputted via the demultiplexer  450 . 
     Example Architecture of Multiple Band Noise Reduction Circuit 
       FIG. 5  is a block diagram illustrating MBNR circuit  420 , according to one embodiment. MBNR circuit  420  receives an input image data  502  and generates noise reduced image data  422 , 432 . MBNR circuit  420  may include, among other components, a noise model circuit  516 , a first photometric distance calculator circuit  526 , a first bilateral filter circuit  536 , a noise model circuit  546 , a texture preservation (TP) circuit  553 , a second photometric distance calculator circuit  556 , and a second bilateral filter circuit  568 . However, in some embodiments, the MBNR  420  does not include the first photometric distance calculator circuit  526  and the first bilateral filter circuit  536 . 
     The noise model circuit  516  receives the image data  502  and calculates the standard deviation of the noise of the pixel values in the image data  502 . The noise standard deviation  520  is then transmitted to the first photometric distance calculator  526 . 
     The first photometric distance calculator circuit  526  computes photometric distances  530  for the neighboring pixels in a block of pixels (e.g., a 3×3 or 5×5 block) from the image date  502 . The computed photometric distances may be normalized by the noise standard deviation  520  generated by the noise model circuit  516 . 
     The first bilateral filter  536  calculates bilateral filter coefficients by multiplying photometric coefficients derived from the photometric distances  530  received from the first photometric distance calculator circuit  526  with the corresponding spatial coefficients of the neighboring pixel locations and performs bilateral filtering on the input image data  502  to produce filtered pixel values  540 . Instead of using the filtered pixel values  540  of the first bilateral filter  536  as the final result of the MBNR circuit  420 , the filtered pixel values  540  are fed to the noise model circuit  546  and subsequent circuits including the second bilateral filter  568  for another iteration of more refined filtering. 
     The noise model  546  determines the noise standard deviation  550  for the filtered pixel values  540 . The noise standard deviation  550  for the filtered pixel values  540  is sent to the second photometric distance calculator  556 . In some embodiments, the noise model  516  and the noise model  546  are the same noise model. In some embodiments, the MBNR  420  only has a single noise model that determines both the noise standard deviation  520  and the noise standard deviation  550 . For a pixel location, the second bilateral filter can use a second number of neighboring pixels. The second number of neighboring pixels can be the same number of neighboring pixels as the first number of neighboring pixels (used in the first bilateral filter  536 ) or can be more than the first number of neighboring circuits. 
     The TP circuit  553  modifies pixel values that are used by the second photometric distance calculator  556  to calculate photometric distances  562 . By using the modified pixel values, the second bilateral filter  568  is biased to reduce edge and/or texture blurring of the image data. For this purpose, the TP circuit  553  receives input image data  502 . In some embodiments, the TP circuit  553  also receives the filtered pixel values  540  from the first bilateral filter  536 . The filtered pixel values  540  may be used as indexing for the lookup table to determine the lower and upper bounds of the offset value (described with respect to  FIG. 6 ). Alternatively, the original center pixel from the image data  502  is used to index the lookup table. For a block of pixels (e.g., 3×3 or 5×5 pixels) that includes a center pixel value and neighboring pixel values, the TP circuit  553  determines a modified center pixel value  555  of the block, and transmits the modified pixel value  555  to the second photometric distance calculator  556 . The modified pixel value  555  and the TP circuit  553  are further described with reference to  FIG. 6 . 
     The second photometric distance calculator circuit  556  computes photometric distances  562  based on the modified center pixel value  555  generated by the TP circuit  553  and the neighboring pixel values (e.g., received from the image data  502 ). Specifically, the second photometric distance calculator circuit  556  computes photometric distances  562  for the neighboring pixels of the block where the original center pixel value of the block is interchanged with the modified pixel value  555  (the block with the modified pixel value  555  may be referred to as a modified pixel block). Other than the use of the modified pixel value  555  instead of the original center pixel value, the second photometric distance calculator circuit  556  performs in a similar way as the first photometric distance calculator circuit  526  to generate photometric distances  562 . 
     The second bilateral filter  568  performs the bilateral filtering on a pixel in the input image data  502  (not modified by the TP circuit  553 ) using the photometric distances  562 . Specifically, the second bilateral filter  568  obtains a tap for a pixel location by multiplying a photometric coefficient derived from the photometric distance for the pixel location with a corresponding spatial coefficient of the pixel location. After obtaining taps for each pixel location of the neighboring pixels in the block surrounding the center pixel to be bilaterally filtered, the second bilateral filter  568  multiplies a pixel value with a corresponding tap, and adds the multiplied values to obtain the bilaterally filtered version of the center pixel. The second bilateral filter  568  outputs, as the output of the MBNR circuit  420 , the noise reduced image data  422 . 
     Although the embodiment of  FIG. 5  shows the filtering the raw image data in two stages, three or more stages of bilateral filtering based on gradually finer noise models may be performed to generate the noise reduced version of the raw image data. Alternatively, only a single stage of bilateral filtering may also be performed. 
     Example Texture Preservation Circuit 
       FIG. 6  is a block diagram illustrating components of a texture preservation (TP) circuit  553 , according to one embodiment. The TP circuit  553  may include, among other components, an offset calculator circuit  605 , a center pixel modifier circuit  620 . The offset calculator circuit  605  includes a high pass filter circuit  610  and a clipping circuit  615 . 
     The TP circuit  553  receives a block of pixels (e.g., a 3×3 or 5×5 block) in the input image data  502  as original pixels, and processes the block of pixels to generate a modified version of the center pixel of the block. In some embodiments, the original pixels are luminance values and in some embodiments the original pixels are red, blue, and green values. The block of pixels includes the center pixel and neighboring pixels that surround the pixel in the image data. 
     The offset calculator circuit  605  determines an offset value  626  for the center pixel of the block of pixels based on the received block of pixels. The offset value is used to modify the center pixel value of the block of pixels. The high pass filter circuit  610  applies a high pass filter to the block of pixels. Specifically, a high pass filter is applied by convoluting a high pass filter kernel with the block of pixel values. The high pass filter may be a Laplacian high pass filter or a Laplacian of Gaussian high pass filter. Applying the high pass filter to the block of pixel values produces an offset value  626 . 
     The clipping circuit  615  ensures that the offset value does not exceed an upper or lower bound by clipping the offset value if it exceeds the upper or lower bound. If the offset value is larger than the upper bound, the offset value is set to the upper bound value, and similarly, if the offset value is smaller than the lower bound, the offset value is set to the lower bound value. The clipping circuit  615  may receive the upper and lower bounds  625  from a look up table  626  (e.g., based on the center pixel value). Entries in the look up table may be determined by the CPU  208  based on characteristics of the image data and the image sensor  202  that captured the image data. 
     The center pixel modifier circuit  620  receives an offset value  626  from the offset calculator circuit  605  and the center pixel of the block of pixels. The center pixel modifier circuit  620  adjusts the center pixel of the block by the offset value to generate a modified center pixel value  555 . For example, the center pixel modifier circuit  620  adds the offset value to the center pixel value. In some embodiments, the center pixel modifier circuit  620  includes a second clipping circuit (not shown in  FIG. 6 ) similar to the clipping circuit  615  to ensure that the modified center pixel value does not exceed the upper or lower bound. 
     Example Method of Filtering with Texture Preservation 
       FIG. 7  is a flowchart illustrating a method of biasing an image noise filter to reduce edge and texture blurring of image data, according to one embodiment. The steps of the method may be performed in different orders, and the method may include different, additional, or fewer steps. 
     An offset calculator circuit receives  702  receives pixel values for a block of original pixels in an image data. The block of original pixels comprises a center pixel and neighboring pixels within a predetermined distance from the center pixel. The block of original pixels may be a 5×5 block of pixels. In some embodiments, the block of original pixels is a 3×3 block of pixels. 
     The offset calculator circuit applies  704  a high pass filter to pixel values of the block of original pixels to generate an offset value for the center pixel. The high pass filter may be a Laplacian high pass filter or a Laplacian of Gaussian high pass filter. The offset value may be clipped to a first value if the offset value is above the first value and clipped to a second value if the offset value is below the second value. 
     A center pixel modifier circuit adjusts  706  a pixel value of the center pixel by the offset value to generate a modified pixel value for the center pixel. The modified pixel is then used to determine photometric distances for a bilateral filter. By using such photometric distances derived from the modified center pixel value, edges and/or texture in the image data can be better preserved after performing the bilateral filtering. 
     The modified center pixel value and the neighboring pixels form a modified pixel block. A photometric distance calculator circuit may determine photometric coefficients by calculating photometric distances of pixel values in the modified pixel block. 
     A bilateral filter may perform bilateral filtering by at least multiplying one of the coefficients derived from the photometric distances, a corresponding spatial coefficient of the block of original pixels, and a pixel value of a corresponding pixel in the block of original pixels. 
     While particular embodiments and applications have been illustrated and described, it is to be understood that the invention is not limited to the precise construction and components disclosed herein and that various modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope of the present disclosure.

Metadata:
Filing Date: 20190424
Publication Date: 20210727
Grant Date: 20210727
Priority Date: 20190424
Inventors: LIN, SHENG
CHENG, Wu
SMIRNOV, MAXIM
Assignee: APPLE INC
CPC Classifications: [{"code": "G06T5/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/20192", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10024", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/20", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T2207/20021", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20192", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10024", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/002", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/20", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T2207/20028", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20192", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T7/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10024", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/70", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/70", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 72916773