PATENT DOCUMENT

Publication Number: US-10692191-B2
Application Number: US-201816100833-A
Country: US
Kind Code: B2

Title: Per-pixel photometric contrast enhancement with noise control

Abstract:
Embodiments relate to enhancing local contrast in an image. A bilateral high pass filter generates a high frequency value for each pixel of an input image, based on a convolution using photometric kernel coefficients associated with other pixels around the pixel. A noise control circuit generates a modulated high frequency value for the pixel based on a noise model for the image defining a noise threshold value for modifying the high frequency value. The modulated high frequency value for the pixel is then combined with a pixel value of the pixel to generate an enhanced value for the pixel. Enhanced values for pixels of the image may be generated to provide the local contrast enhancement for the input image.

Claims:
What is claimed is: 
     
       1. An apparatus for enhancing an image, comprising:
 a bilateral high pass filter configured to generate a high frequency value for each of pixels in the image by performing a convolution between pixel values of other pixels around each pixel and photometric kernel coefficients associated with the other pixels, the high frequency value corresponding to photometric contrast between each pixel and the other pixels, the photometric kernel coefficients determined using photometric distances between a pixel value of each pixel and the pixel values of the other pixels; 
 a noise control circuit coupled to the bilateral high pass filter, the noise control circuit configured to generate a modulated high frequency value for each pixel based on a noise model for the image defining a noise threshold value for modifying the high frequency value; and 
 a combiner coupled to the noise control circuit, the combiner configured to generate an enhanced value for each pixel by combining the modulated high frequency value for each pixel with the pixel value of each pixel. 
 
     
     
       2. The apparatus of  claim 1 , wherein:
 the apparatus further comprises a buffer coupled to the bilateral high pass filter, the buffer configured to receive a patch of the image comprising each pixel and the other pixels; and 
 the bilateral high pass filter is further configured to perform bilateral high pass filtering of a single color component of the patch of the image based on the convolution with the photometric kernel coefficients to generate the high frequency value for each pixel. 
 
     
     
       3. The apparatus of  claim 1 , wherein the noise control circuit is further configured to:
 determine the noise threshold value based on the noise model and at least one color component of the image; 
 determine a sharpening coefficient based at least in part on the high frequency value for each pixel and the noise threshold value; and 
 scale the high frequency value for each pixel with the sharpening coefficient to obtain the modulated high frequency value for each pixel. 
 
     
     
       4. The apparatus of  claim 3 , wherein the combiner is further configured to:
 add the modulated high frequency value for each pixel to a single color component of the pixel value of each pixel to generate the enhanced value for each pixel. 
 
     
     
       5. The apparatus of  claim 3 , wherein:
 the sharpening coefficient is smaller than a defined value, if the high frequency value for each pixel is less than the noise threshold value; and 
 the sharpening coefficient is equal to the defined value, if the high frequency value for each pixel is larger than the noise threshold value. 
 
     
     
       6. The apparatus of  claim 1 , wherein the apparatus further comprises a photometric processor configured to:
 determine the photometric distances; and 
 determine the photometric kernel coefficients using the photometric distances. 
 
     
     
       7. The apparatus of  claim 1 , wherein the image comprises an unscaled version of an input image having a pixel resolution equal to a pixel resolution of the input image. 
     
     
       8. The apparatus of  claim 1 , wherein the image comprises a downscaled version of an input image having a pixel resolution lower than a pixel resolution of the input image. 
     
     
       9. The apparatus of  claim 1 , further comprising a radial gain calculator circuit coupled to the noise control circuit and the combiner, the radial gain calculator circuit configured to:
 determine a radial gain based on a position of each pixel relative to a center region of the image; and 
 adjust the modulated high frequency value for each pixel based on the radial gain. 
 
     
     
       10. The apparatus of  claim 9 , wherein the combiner is further configured to:
 add the adjusted modulated high frequency value for each pixel to a single color component of the pixel value of each pixel to generate the enhanced value for each pixel. 
 
     
     
       11. The apparatus of  claim 1 , wherein the noise control circuit is further configured to determine the noise model using a look-up table stored in a memory of the apparatus, the look-up table defining frequency values as a function of pixel values of the image for one or more color components. 
     
     
       12. A method for enhancing an image, comprising:
 generating a high frequency value for each of pixels in the image by performing a convolution between pixel values of other pixels around each pixel and photometric kernel coefficients associated with the other pixels, the high frequency value corresponding to photometric contrast between each pixel and the other pixels, the photometric kernel coefficients determined using photometric distances between a pixel value of each pixel and the pixel values of the other pixels; 
 generating a modulated high frequency value for each pixel based on a noise model for the image defining a noise threshold value for modifying the high frequency value; and 
 generating an enhanced value for each pixel by combining the modulated high frequency value for each pixel with the pixel value of each pixel. 
 
     
     
       13. The method of  claim 12 , further comprising:
 receiving a patch of the image comprising each pixel and the other pixels; and 
 performing bilateral high pass filtering of a single color component of the patch of the image based on the convolution with the photometric kernel coefficients to generate the high frequency value for each pixel. 
 
     
     
       14. The method of  claim 12 , further comprising:
 determining the noise threshold value based on the noise model and at least one color component of the image; 
 determining a sharpening coefficient based at least in part on the high frequency value for each pixel and the noise threshold value; and 
 scaling the high frequency value for each pixel with the sharpening coefficient to obtain the modulated high frequency value for each pixel. 
 
     
     
       15. The method of  claim 14 , further comprising:
 adding the modulated high frequency value for each pixel to a single color component of the pixel value of each pixel to generate the enhanced value for each pixel. 
 
     
     
       16. The method of  claim 12 , further comprising:
 determining a radial gain based on a position of each pixel relative to a center region of the image; and 
 adjusting the modulated high frequency value for each pixel based on the radial gain. 
 
     
     
       17. The method of  claim 16 , further comprising:
 adding the adjusted modulated high frequency value for each pixel to a single color component of the pixel value of each pixel to generate the enhanced value for each pixel. 
 
     
     
       18. A system, comprising:
 an image sensor configured to obtain an image having a plurality of color components; 
 an image signal processor coupled to the image sensor, the image signal processor configured to perform de-noising of the image to obtain a de-noised version of the image having one or more color components of the plurality of color components, the image signal processor including:
 a bilateral high pass filter configured to generate a high frequency value for each of pixels in the de-noised version of the image by performing a convolution between pixel values of other pixels around each pixel and photometric kernel coefficients associated with the other pixels, the high frequency value corresponding to photometric contrast between each pixel and the other pixels, the photometric kernel coefficients determined using photometric distances between a pixel value of each pixel and the pixel values of the other pixels, 
 a noise control circuit coupled to the bilateral high pass filter, the noise control circuit configured to generate a modulated high frequency value for each pixel based on a noise model for the image defining a noise threshold value for modifying the frequency value, and 
 a combiner coupled to the noise control circuit, the combiner configured to generate an enhanced value for each pixel by combining the modulated high frequency value for each pixel with the pixel value of each pixel. 
 
 
     
     
       19. The system of  claim 18 , wherein:
 the noise control circuit is further configured to:
 determine the noise threshold value based on the noise model and at least one color component of the image, 
 determine a sharpening coefficient based at least in part on the high frequency value for each pixel and the noise threshold value, and 
 scale the high frequency value for each pixel with the sharpening coefficient to obtain the modulated high frequency value for each pixel; and 
 
 the combiner is further configured to add the modulated high frequency value for each pixel to a single color component of the pixel value of each pixel to generate the enhanced value for each pixel.

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates a circuit for processing images and more specifically to per-pixel photometric contrast enhancement with noise control. 
     2. Description of the Related Arts 
     Image data captured by an image sensor or received from other data sources is often processed prior to 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. Performing the image processing on a device uses system resources. For example, image processing algorithms may be performed by executing software programs on a central processing unit (CPU). However, the execution on the CPU consumes resources of the CPU and memory, and can interfere with processing of other tasks or increase power consumption. 
     SUMMARY 
     Embodiments of the present disclosure relate to local contrast enhancement of an image. To perform the local contrast enhancement for an image, a high frequency value for each pixel of the image is generated based on a convolution using photometric kernel coefficients associated with other pixels in a patch defined around the pixel. A modulated high frequency value for each pixel is generated based on a noise model for the image defining a noise threshold value for modifying the high frequency value. An enhanced value for each pixel is generated by combining the modulated high frequency value for each pixel with a pixel value of each pixel to produce an output image with local contrast enhancement. 
    
    
     
       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 local contrast enhancement circuit, according to one embodiment. 
         FIG. 5  is a detailed block diagram illustrating a local contrast enhancement circuit, according to one embodiment. 
         FIG. 6  is a graph illustrating a noise control in a local contrast enhancement circuit, according to one embodiment. 
         FIG. 7  is a flowchart illustrating a method of local contrast enhancement, 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 enhancing local contrast in an image. The local contrast for a pixel in the image is a luminosity ratio between a brightest pixel and a darkest pixel in a defined vicinity of the pixel (i.e., in a window around a pixel). Thus, the local contrast for the pixel depends upon a size of the window around the pixel. The approach presented in this disclosure enhances the local contrast for a wide range of window sizes. A bilateral high pass filter generates a high frequency value for each pixel of the image, based on a convolution using photometric kernel coefficients associated with other pixels in a patch or window around the pixel. The high frequency value for the pixel is indicative of a level of photometric contrast between the pixel and other pixels in the patch around the pixel. A noise control circuit generates a modulated high frequency value for the pixel based on a noise model for the image defining a noise threshold value for modifying the high frequency value to prevent amplification of noise associated with the pixel. The modulated high frequency value for the pixel is then combined with a pixel value of the pixel to generate an enhanced value for the pixel. Enhanced values for pixels of the image are generated to provide the local contrast enhancement for the image. 
     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 . The device  100  may include components not shown in  FIG. 1 . 
     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 components 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). 
       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 sensor  202  is a component for capturing image data and may be embodied, for example, as a complementary metal-oxide-semiconductor (CMOS) active-pixel sensor) a camera, video camera, or other devices. Image sensor  202  generates 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 sensor  202  may be in a Bayer color filter array (CFA) pattern (hereinafter also referred to as “Bayer pattern”). 
     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 , 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  106  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  108  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 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 . 
     Sensor interface  212  is circuitry for interfacing with motion sensor  234 . 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  210  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 sensor  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 sensor  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 image sensor  202  to receive raw image data. 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 , and output interface  316 . ISP  206  may include other components not illustrated in  FIG. 3  or may omit one or more components illustrated in  FIG. 3 . 
     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 (i.e., 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 special image effects, including black and white conversion, sepia tone conversion, negative conversion, or solarize conversion. 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 Y, Cb, and Cr 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, resample processing stage  308  converts RBD format into YCbCr 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, mask 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), auto focus (AF)), 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 (e.g., AF statistics) 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 . 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 cameral 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 image 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 provide 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 special image effects. 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 (i.e. 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 is not a spatially filtered reference frame). 
     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, local contrast enhancement, 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. Local contrast enhancement may be applied to enhance local photometric contrasts in image data. 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, 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 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 system  100 , as discussed above with regard to  FIGS. 1 and 2 . 
     In various embodiments, the functionally of components  302  through  342  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 Local Contrast Enhancement Circuit 
       FIG. 4  is a block diagram illustrating a portion of the image processing pipeline including a local contrast enhancement (LCE) circuit  404 , according to one embodiment. In the embodiment of  FIG. 4 , the LCE circuit  404  is part of color processing stage  312  that also includes, among other components, a sub-band merger (SBM) circuit  402  and a Local Tone Mapping (LTM) circuit  403 . In some other embodiments (not shown in  FIG. 4 ), the LCE circuit  404  can be part of noise processing stage  310 . The LCE circuit  404  enhances local photometric contrast in image data, such as in de-noised image data. 
     Circuitry of the noise processing stage  310  applied prior to the color processing stage  312  and the LCE circuit  404  removes a certain level of noise from the image data. However, some residual noise still remains, e.g., in image data  406  being output from the noise processing stage  310  and input into the color processing stage  312  and later into the LCE circuit  404  after being processed by the SBM circuit  402  and the LTM circuit  403 . The LCE circuit  404  performs sharpening and enhancing of local photometric contrast in de-noised image data such that the residual noise is not amplified. Contrast enhancement performed by the LCE circuit  404  may be applied only to the luminance component (i.e., Y color component) of the de-noised image data. In other examples, the contrast enhancement is also applied to one or more other components, such as the chroma components Cb and Cr. In some embodiments, an image in another format is converted to the YCbCr format prior to processing by the LCE circuit  404 . 
     Color processing stage  312  receives image data  406  with reduced noise from noise processing stage  310  in  FIG. 3 . Image data  406  may include one or more color components of a plurality of color components (e.g., Y, Cb and Cr color components). In some embodiments, image data  406  comprises a processed (e.g., de-noised) unscaled version of an input image of a single color component (e.g., Y color component) having a pixel resolution equal to a pixel resolution of the input image. In some other embodiments, image data  406  comprises a processed (e.g., de-noised) downscaled version of the input image having a pixel resolution lower than a pixel resolution of the input image. 
     In some embodiments, when the image data  406  comprises the processed unscaled version of the input image of the single color component, the SBM circuit  402  merges processed high frequency component of unscaled image data  408  and processed low frequency component of unscaled image data  408  to generate merged unscaled image data  409  having the single color component. The LTM circuit  403  may apply local tone mapping on the unscaled image data  409  to generate merged unscaled image data  410  having the single color component. The LCE circuit  404  applies local contrast enhancement on the single color component of merged unscaled image data  410  to generate enhanced unscaled image data  412  of the single color component, which may be provided as image data  414  to output rescale stage  314  in  FIG. 3 . 
     In some other embodiments, when the image data  406  is the processed downscaled version of the input image, the SBM circuit  402  merges processed high frequency component of downscaled image data  408  and processed low frequency component of downscaled image data  408  of a first pixel resolution to generate processed low frequency component image data  409  having a second pixel resolution higher than the first pixel resolution. The processed low frequency component image data  409  may then be processed by the LTM circuit  403  and the LCE circuit  404  and then fed back to the SBM circuit  402  as image data  416  via demultiplexer  418  and multiplexer  420 . The image data  416  is then merged with high frequency component of image data  408  of the second pixel resolution. The process of combining the processed high frequency component image data and the processed low frequency component image data is repeated until the SBM circuit  402  and the LCE circuit  404  generates a processed version  414  of input image that is outputted via demultiplexer  418 , e.g., for merging with an unscaled version of input image. The LCE circuit  404  may perform local contrast enhancement on a single color component (e.g., Y color component) of one or more versions of processed low frequency component image data  410 , wherein each version of processed low frequency component image data  410  has a different pixel resolution lower than the pixel resolution of the input image. Hence, the LCE circuit  404  may be bypassed for certain scales of downscaled image data  410 . 
     In some embodiments, the LTM circuit  403  applies local tone mapping on image data  409  representing scale M of input image, e.g., M=1 and image data  409  is a first downscaled version of input image. The LCE circuit  404  may apply local contrast enhancement on image data  410  representing any scale of input image, e.g., from scale M to the finest scale N, where N≥M. For each scale of input image, the LCE circuit  404  may optimize local contrast in a corresponding support window of pixels of input image, wherein a size of the support window depends on a processed scale of input image. 
     In some embodiments, the LCE circuit  404  enhances local photometric contrast of a single color component (e.g., Y color component) of image data  410  that is a processed unscaled version of the input image. In some other embodiments, the LCE circuit  404  enhances contrast of a single color component (e.g., Y color component) of image data  410  that is a processed downscaled version of the input image. The LCE circuit  404  enhances contrast of image data  410  based on high pass bilateral filtering followed by signal modulation circuitry that provides noise control. More details about structure and operation of the LCE circuit  404  are provided below in detail in conjunction with  FIGS. 5 through 7 . 
     Local Contrast Enhancement Circuit 
       FIG. 5  is a detailed block diagram illustrating the LCE circuit  404  of  FIG. 4 , according to one embodiment. Buffer  502  receives and stores pixel values for a plurality of pixels of at least a portion of image  506 . Image  506  may be a processed (e.g., de-noised) version of the input image. Image  506  may comprise a single color component (e.g., Y color component) of an unscaled version of the input image having a pixel resolution equal to a pixel resolution of the input image. Alternatively, image  506  comprises a plurality of color components (e.g., Y, Cb and Cr color components) of an unscaled version of the input image having a pixel resolution equal to a pixel resolution of the input image. Alternatively, image  506  comprises a plurality of color components (e.g., Y, Cb and Cr color components) of downscaled version of the input image having a pixel resolution lower than the pixel resolution of the input image. At least a portion of the pixel values stored into the buffer  502  corresponding to a patch or convolution window of image  506  are provided as a plurality of pixel values  508  to bilateral high pass filter  510  and photometric distance calculator  512 . 
     For each center pixel of a patch, the photometric distance calculator  512  calculates photometric distances  514  between a pixel value of the pixel and other pixel values of other pixels of the patch. The pixel value may be associated with the pixel having a central position in the patch of image  506 , and the other pixel values may be associated with other pixels in the patch of image  506  around the pixel. The patch used for image  506  can be of square shape having spatial width and length of size n, wherein n can be, e.g., 3, 5, 7, etc. For example, a patch may include a 5×5 array of pixels. In one embodiment, the photometric distances  514  are calculated using a single color component (e.g., Y color component) of the pixel value and the other pixel values. In another embodiment, the photometric distances  514  are calculated using a plurality of color components (e.g., Cb, Cr and Y color components) of the pixel value and the other pixel values. 
     Photometric kernel calculator  516  coupled to photometric distance calculator  512  calculates photometric kernel coefficients  518 , based on the photometric distances  514 . The photometric kernel coefficients  518  represent measure of photometric similarity between the pixel and the other pixels around the pixel in the patch of image  506 . Photometric kernel calculator  516  calculates the photometric kernel coefficients  518  such that values for the photometric kernel coefficients  518  are suitable for mitigating an overshot in the patch of image  506 . 
     In some embodiments, the photometric kernel coefficients  518  can be computed based on the photometric distances  514  as follows:
 
 W   p [ n,m ]= G (MD[ n,m ])  Equation 1
 
where MD[n, m] represents a photometric distance  514  (e.g., Mahalanobis distance) between the pixel and a [n, m] pixel in its vicinity within the patch of image  506 ; G represents any non-linear function (usually Gaussian). In one embodiment, the photometric kernel coefficients  518  can be computed as follows:
 
 W   p [ n,m ]=1−min(1,Tmp×Slope)  Equation 2
 
Tmp=max(0, k [ n,m ]MD[ n,m ]−Knee)  Equation 3
 
where Knee and Slope are function parameters, and k[n, m] represents a spatial adjustment coefficient.
 
     The photometric kernel coefficients  518  obtained by photometric kernel calculator  516  are provided to bilateral high pass filter  510  for performing convolution with the pixel values  508  of plurality of pixels in the patch of image  506 . In some embodiments, photometric distance calculator  512  and photometric kernel calculator  516  are separate blocks integrated into a photometric processor  520 . In some other embodiments (not shown in  FIG. 5 ), photometric distance calculation and photometric kernel calculation can be performed by a single circuit (e.g., CPU) of the photometric processor  520 . 
     Bilateral high pass filter  510  performs convolution between a single color component (e.g., Y color component) of the pixel values  508  and the photometric kernel coefficients  518  to generate a high frequency value  522  indicative of photometric contrast between the pixel and the other pixels around the pixel in the patch of image  506 . Bilateral high pass filter  510  combines domain and range filtering, thereby enforcing both photometric and geometric locality. Bilateral high pass filter  510  generates the high frequency value  522  of the pixel based on similar and nearby pixel values in the patch of image  506 . The convolution may include multiplication of pixel values  508  for pixels of the patch with corresponding photometric kernel coefficients  518  to generate multiplied values, and then addition of the multiplied values to generate the high frequency value  522 . 
     Bilateral high pass filter  510  calculates the high frequency value  522  as follows:
 
HP 0 =Σ i Σ j (   P     0   − P     ij ) C   ij   = P     0 −Σ i Σ j     P     ij   C   ij   = P     0 − LP   0   Equation 4
 
wherein HP 0  is the high frequency value  522  for the pixel,  P   0  is a photometric intensity (e.g., an intensity of luminance component) of the pixel,  P   ij  is photometric intensity (e.g., an intensity of luminance component) of a pixel [i, j] in the patch of image  506  around the pixel, C i,j  is a bilateral filter coefficient (after normalization) associated with the pixel [i, j], i.e., the photometric kernel coefficient  518  for the pixel [i, j] determined based on a photometric distance  514  for the pixel [i, j], and  LP   0  represents a low-pass filtered version of the pixel. Thus, according to Equation 4, the high frequency value  522  for the pixel can be defined as a difference between the photometric intensity of the pixel and the low-pass filtered version of the pixel.
 
     As discussed, the LCE circuit  404  may optimize local contrast for each scale of input image within a corresponding support window of pixels of input image. In some embodiments, bilateral high pass filter  510  processes the pixel values  508  in the patch of image  506  of size 5×5. The LCE circuit  404  then enhances local contrast in 5×5 region of input image when image  506  is an unscaled version of input image (i.e., scale 0). If image  506  is a first downscaled version of input image (i.e., scale 1), the LCE circuit  404  enhances local contrast in 10×10 region of input image. If image  506  is a second downscaled version of input image having a lower pixel resolution than the first downscaled version (i.e., scale 2), the LCE circuit  404  enhances local contrast in 20×20 region of input image, and so on for other scales. 
     The high frequency value  522  generated by bilateral high pass filter  510  is indicative of a level of contrast between the pixel and the other pixels around the pixel in the patch of image  506 . The high frequency value  522  is larger if the level of contrast in the patch of image  506  is higher, e.g., when the pixel is located at a boundary between two regions of different brightness when processing is performed on the Y channel. As such, the high frequency value  522  having a value larger than a defined threshold value indicates that the variation at the pixel location is large enough to be considered being above a noise threshold and, therefore, can be amplified to increase the local contrast. In this case, the LCE circuit  404  further enhances contrast of the pixel relative to the other pixels, e.g., by adjusting the pixel value of the pixel with a larger modulated high frequency value. In contrast, when the pixel is located in a smooth region where pixel values are similar to each other (i.e., variations in the region are less than the residual noise level), the high frequency value  522  is smaller. As such, the high frequency value  522  having a value below a defined threshold value indicates that a brightness of the pixel does not substantially differ from an average brightness in the patch of image  506 . In this case, the LCE circuit  404  adjusts the pixel value of the pixel with a modulated high frequency value smaller than it would be if the high frequency value  522  was larger than the defined threshold value. 
     The high frequency value  522  generated by bilateral high pass filter  510  is provided to noise control circuitry  524 . Noise control circuitry  524  generates a modulated high frequency value  526  for the pixel based on a noise model for the image  506  defining a noise threshold value  528  for modifying the high frequency value  522  that prevents amplification of residual noise associated with the pixel. This may be achieved by attenuating the high frequency value  522  when the high frequency value  522  is below the threshold value  528 , and/or amplifying the high frequency value  522  when the high frequency value  522  exceeds the noise threshold value  528 . 
     Noise control circuitry  524  includes noise model circuit  530  and sharpening coefficient calculator  532 . Noise model circuit  530  can be implemented as a storage device (i.e., memory) that stores the noise model. Noise model circuit  530  may store the noise model as a look-up table representing threshold frequency values as a function of pixel values of the image  506 , wherein the function depends on one or more components associated with the pixel values. In one example, the function for the threshold frequency value uses the Y component. In another example, the function uses the Y, Cr, and Cb components, or some other combination of color components. Noise model circuit  530  determines the noise threshold value  528  based on the noise model stored in the noise model circuit  530  and at least one color component (e.g., Y color component) of one or more pixel values  534  of the image  506 . 
     Sharpening coefficient calculator  532  determines a modulation signal  536  (i.e., sharpening coefficient) for modulating the high frequency value  522 , based at least in part on the high frequency value  522  and the noise threshold value  528 . The modulated high frequency value  526  for the pixel is generated by modulating (e.g., scaling) the high frequency value  522  with the modulation signal  536  (sharpening coefficient). Multiplier  504  may perform scaling (i.e., multiplying) of the high frequency value  522  with the modulation signal  536  to generate the modulated high frequency value  526 . The modulation signal  536  increases as the high frequency value  522  increases until the high frequency value  522  reaches the noise threshold value  528 . The modulated signal  536  is equal to a defined maximum value for the sharpening coefficient when the high frequency value  522  exceeds the threshold value  528 . Below the noise threshold value  528 , the modulation signal  536  attenuates relative to the maximum value to prevent the noise amplification, e.g., when the high frequency value  522  is small due to residual noise. When the high frequency value  522  exceeds the noise threshold value  528  (e.g., due to a residual noise related to the pixel), the modulation signal  536  is limited to the defined maximum value to prevent unbounded amplification of the high frequency value  522 , i.e., to prevent amplification of the residual noise. Having the modulation signal  536  like that provides gradual increase in contrast amplification from no amplification or small amplification in noisy regions to a full desired amplification for image details above a noise threshold level. 
       FIG. 6  is a graph  600  illustrating determining the modulation signal  536  (sharpening coefficient) for noise control, according to one embodiment. Sharpening coefficient calculator  532  determines the modulation signal  536  in accordance with the graph  600 . As shown by the graph  600 , the modulation signal  536  is a function of the high frequency value  522  and the modulation signal  536  further depends on the noise threshold value  528  and the defined maximum value for the sharpening coefficient. For the high frequency value  522  being less than the noise threshold value  528 , the modulation signal  536  increases as the high frequency value  522  increases starting with a value of Kmin (e.g., 0&lt;Kmin&lt;Kmax). When the high frequency value  522  reaches and exceeds the noise threshold value  528 , the modulation signal  536  does not increase further and equals to the defined maximum value, e.g., Kmax defined by a desired contrast enhancement level. Put another way, the modulation signal  536  provides an attenuation relative to the maximum value Kmax for high frequency values  522  below the noise threshold value  528 . In one example, Kmin is 0 and Kmax is 1 to provide attenuation below the noise threshold value  528 . The values of Kmin and Kmax may vary, and in some embodiments, may be provided by the noise model of the noise model circuit  530  using an analysis of one or more color components of the image  506 . 
     Referring back to  FIG. 5 , the modulated high frequency value  526  generated by noise control circuit  524  (or some adjusted version of modulated high frequency value  526 ) is combined with a single color component (e.g., Y color component) of pixel value  538  of the pixel to generate an enhanced value  540  for the pixel. The modulated high frequency value  526  and the enhanced value  540  are larger if the level of photometric contrast associated with the pixel is larger, thus providing local contrast enhancement with noise control for the pixel. The noise model circuit  530  ensures that residual noise in the image  506  is not amplified. Furthermore, the bilateral high pass filter  510  ensures that the high frequency value  522  is below an upper bound to prevent “overshoot” of the image  506 . 
     Referring back to equation 4, local contract enhancement performed by the LCE circuit  404  mixes the high frequency value HP 0  into a single color component pixel value (e.g., pixel luminance)  P   0  as follows:
 
   P     0   enh   = P     0   +K *HP 0   Equation 5
 
where K is the modulated high frequency value  526  and  P   0   enh  is the enhanced value  540  for the pixel. Thus, if the modulated high frequency value  526  is larger, then more high frequency contribution is included in the enhanced value  540  for the pixel.
 
     In some embodiments, the LCE circuit  404  includes radial gain calculator  542  that determines a radial gain  544  for lens compensation, based on information about position of the pixel in the patch of image  506  relative to a central axis of the input image. Images captured through lenses may be distorted depending on lens characteristics, and the radial gain  544  can adjust for the distortions resulting from the lens. For example, lens distortion may increase moving away from a center region of an image. Thus, a radial gain  544  for each pixel may be computed based on the distance of the pixel from a center region of the image  506 , and a scaling factor applied to the distance. Multiplier  552  may perform scaling (i.e., multiplying) of the modulated high frequency value  526  for the pixel with the radial gain  544  to generate an adjusted modulated high frequency value  548  for the pixel. Combiner  550  may then add the adjusted modulated high frequency value  548  to the single color component of pixel value  538  to generate the enhanced value  540  for the pixel. This process of local contrast enhancing can be performed for multiple (e.g., all) pixels in image  506 . In each cycle, a different patch and center pixel may be processed until an enhanced value  540  is generated for each pixel of image  506 . 
     Example Process for Performing Local Contrast Enhancement 
       FIG. 7  is a flowchart illustrating a method of local contrast enhancement, according to one embodiment. The method may include additional or fewer steps, and steps may be performed in different orders. The LCE circuit  404 , as described with reference to  FIG. 5 , generates  710  a high frequency value for a pixel of an image based on a convolution using photometric kernel coefficients associated with other pixels around the pixel. The LCE circuit  404  receives (e.g. via buffer  502 ) a patch of the image comprising the pixel and the other pixels. The LCE circuit  404  performs (e.g., via bilateral high pass filter  510 ) the convolution between a single color component of pixel values of pixels in the patch and the photometric kernel coefficients to generate the high frequency value indicative of photometric contrast of the pixel relative to the other pixels around the pixel. 
     The LCE circuit  404  generates  720  (e.g., via noise control circuit  524 ) a modulated high frequency value for the pixel based on a desired strength and a noise model defining a noise threshold value for modifying the high frequency value. The LCE circuit  404  determines (e.g., via noise model circuit  530 ) the noise threshold value based on the noise model and at least one color component of the pixels of the image. The LCE circuit  404  further determines (e.g., via sharpening coefficient calculator  532 ) a modulation signal (sharpening coefficient), based at least in part on the high frequency value for the pixel and the noise threshold value. The LCE circuit  404  then modulates the high frequency value for the pixel with the modulation signal to obtain the modulated high frequency value for the pixel. 
     The LCE circuit  404  generates  730  (e.g., via combiner  538 ) an enhanced value for the pixel by combining the pixel value of the pixel with the modulated high frequency value for the pixel. The LCE circuit  404  adds the modulated high frequency value for the pixel to a single color component of the pixel value of the pixel to generate the enhanced value for the pixel. In some embodiments, the LCE circuit  404  determines a radial gain based on a position of the pixel relative to a central axis of the image, and adjusts the modulated high frequency value based on the radial gain. The LCE circuit  404  then combines (e.g., adds) the adjusted modulated high frequency value to a single color component of the pixel value of the pixel to generate the enhanced value for the pixel. 
     The method may be repeated for the pixels of the image to generate an enhanced value for each pixel. In some embodiments, the method is performed to provide local contrast enhancement for the Y component of the image. In some embodiments, the method is repeated to generate multiple channels of enhanced values for each pixel, each for a different color component of the input image. 
     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: 20180810
Publication Date: 20200623
Grant Date: 20200623
Priority Date: 20180810
Inventors: SMIRNOV, MAXIM
Assignee: APPLE INC
CPC Classifications: [{"code": "G06T5/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/40", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/003", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/008", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/002", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/40", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/90", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/70", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/73", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/94", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 69406248