PATENT DOCUMENT

Publication Number: US-11863889-B2
Application Number: US-202217734576-A
Country: US
Kind Code: B2

Title: Circuit for correcting lateral chromatic abberation

Abstract:
Embodiments relate to lateral chromatic aberration (LCA) recovery of raw image data generated by image sensors. A chromatic aberration recovery circuit performs chromatic aberration recovery on the raw image data to correct the resulting LCA in the full color images using pre-calculated offset values of a subset of colors of pixels.

Claims:
The invention claimed is: 
     
       1. An image processor comprising:
 a correction circuit configured to receive pixel values of pixels of a color in a raw input image data, and generate corrected versions of the pixel values by performing interpolation of subsets of the pixels of the color in a plurality of directions using a plurality of different sets of interpolation coefficients, each set of the plurality of different sets of interpolation coefficients associated with a corresponding direction from the plurality of directions, and each set of the plurality of different sets of interpolation coefficients including offset values representing distances from the pixels to corresponding virtual pixels in the corresponding direction where the corresponding virtual pixels have pixel values that are identical to the pixel values of the pixels in the raw input image data absent lateral chromatic aberrations, wherein the corrected versions of the pixel values are included as part of a corrected raw image data; and 
 a memory coupled to the correction circuit, the memory configured to store one or more lookup tables that include the plurality of different sets of interpolation coefficients. 
 
     
     
       2. The image processor of  claim 1 , wherein the correction circuit comprises:
 a first correction circuit configured to:
 perform interpolation of pixel values of a first subset of pixels of the color arranged in a first direction from the plurality of directions using one or more first interpolation coefficients included in a first set of interpolation coefficients from the plurality of different sets of interpolation coefficients to generate first corrected versions of the pixel values, the one or more first interpolation coefficients corresponding to first offset values representing first distances from the pixels to the corresponding virtual pixels in the first direction; and 
 
 a second correction circuit configured to:
 receive the first corrected versions; and 
 generate second corrected versions of the pixel values by performing interpolation of the first corrected versions of the pixel values of second subset of pixels of the color arranged in a second direction from the plurality of directions using one or more second interpolation coefficients included in a second set of interpolation coefficients from the plurality of different sets of interpolation coefficients, the one or more second interpolation coefficients corresponding to second offset values representing second distances from the pixels in the raw input image data to the corresponding virtual pixels in the second direction. 
 
 
     
     
       3. The image processor of  claim 2 , wherein the corrected versions of the pixel values that are included as part of the corrected raw image data include the second corrected versions of the pixel values. 
     
     
       4. The image processor of  claim 2 , further comprising:
 an offset interpolator circuit configured to determine the first offset values and the second offset values of the pixels in the raw input image data by bilateral interpolating predetermined first offset values and predetermined second offset values associated with grid points neighboring the pixels in the raw input image data. 
 
     
     
       5. The image processor of  claim 4 , wherein the one or more lookup tables include:
 a first phase look-up table configured to store the first set of interpolation coefficients indexed by the first distances or first parameters derived from the first distances; 
 a second phase look-up table configured to store the second set of interpolation coefficients indexed by the second distances or second parameters derived from the second distances; and 
 an offset look-up table configured to store the predetermined first offset values and the predetermined second offset values associated with the grid points. 
 
     
     
       6. The image processor of  claim 1 , wherein each of the raw input image data and the corrected raw image data is in a Bayer pattern. 
     
     
       7. The image processor of  claim 2 , wherein the first direction is a vertical direction and the second direction is a horizontal direction. 
     
     
       8. The image processor of  claim 7 , wherein a first corrected version of a pixel value for a pixel in the raw input image data is generated by interpolating pixel values for a number of pixels in a same column as the pixel, and a second corrected version of the pixel value for the pixel is generated by interpolating first corrected versions of pixel values for the same number of pixels in a same row as the pixel. 
     
     
       9. The image processor of  claim 1 , wherein the received pixel values include pixel values of pixels in colors of red, green, and blue, and wherein the pixel values of pixels of two of the colors are updated by the correction circuit, and pixel values of pixels of a remaining one of the colors is not updated by the correction circuit. 
     
     
       10. The image processor of  claim 1 , wherein the color is blue or red, and wherein pixel values of green pixels are not updated by the correction circuit. 
     
     
       11. The image processor of  claim 2 , wherein the interpolation performed by the first correction circuit uses a first function defined by the one or more first interpolation coefficients, and the second correction circuit uses a second function defined by the one or more second interpolation coefficients. 
     
     
       12. A method comprising:
 receiving pixel values of pixels of a color in a raw input image data; and 
 generating corrected versions of the pixel values by performing interpolation of subsets of the pixels of the color in a plurality of directions using a plurality of different sets of interpolation coefficients, each set of the plurality of different sets of interpolation coefficients associated with a corresponding direction from the plurality of directions, and each set of the plurality of different sets of interpolation coefficients including offset values representing distances from the pixels to corresponding virtual pixels in the corresponding direction where the corresponding virtual pixels have pixel values that are identical to the pixel values of the pixels in the raw input image data absent lateral chromatic aberrations, 
 wherein the corrected versions of the pixel values are included as part of a corrected raw image data. 
 
     
     
       13. The method of  claim 12 , wherein generating the corrected versions of the pixel values comprises:
 performing interpolation of pixel values of a first subset of pixels of the color arranged in a first direction from the plurality of directions using one or more first interpolation coefficients included in a first set of interpolation coefficients from the plurality of different sets of interpolation coefficients to generate first corrected versions of the pixel values, the one or more first interpolation coefficients corresponding to first offset values representing first distances from the pixels to the corresponding virtual pixels in the first direction; and 
 generating second corrected versions of the pixel values by performing interpolation of the first corrected versions of the pixel values of second subset of pixels of the color arranged in a second direction from the plurality of directions using one or more second interpolation coefficients included in a second set of interpolation coefficients from the plurality of different sets of interpolation coefficients, the one or more second interpolation coefficients corresponding to second offset values representing second distances from the pixels in the raw input image data to the corresponding virtual pixels in the second direction. 
 
     
     
       14. The method of  claim 13 , further comprising:
 determining the first offset values and the second offset values of the pixels in the raw input image data by bilateral interpolating predetermined first offset values and predetermined second offset values associated with grid points neighboring the pixels in the raw input image data. 
 
     
     
       15. The method of  claim 14 , further comprising:
 storing, in an offset look-up table, the predetermined first offset values and the predetermined second offset values associated with the grid points; 
 storing, in a first phase look-up table, the first set of interpolation coefficients indexed by the first distances or first parameters derived from the first distances; and 
 storing, in a second phase look-up table, the second set of interpolation coefficients indexed by the second distances or second parameters derived from the second distances. 
 
     
     
       16. The method of  claim 12 , wherein each of the raw input image data and the corrected raw image data is in a Bayer pattern. 
     
     
       17. The method of  claim 13 , wherein the first direction is a vertical direction and the second direction is a horizontal direction. 
     
     
       18. The method of  claim 17 , wherein a first corrected version of a pixel value for a pixel in the raw input image data is generated by interpolating pixel values for a number of pixels in a same column as the pixel, and a second corrected version of the pixel value for the pixel is generated by interpolating first corrected versions of pixel values for the same number of pixels in a same row as the pixel. 
     
     
       19. The method of  claim 12 , wherein the received pixel values comprise pixel values of pixels in colors of red, green, and blue, and wherein the pixel values of pixels of two of the colors are updated and pixel values of pixels of a remaining one of the colors is not updated. 
     
     
       20. A system comprising:
 an image sensor configured to generate raw input image data; and 
 an image processor comprising:
 a correction circuit configured to receive pixel values of pixels of a color in the raw input image data, and generate corrected versions of the pixel values by performing interpolation of subsets of the pixels of the color in a plurality of directions using a plurality of different sets of interpolation coefficients, each set of the plurality of different sets of interpolation coefficients associated with a corresponding direction from the plurality of directions, and each set of the plurality of different sets of interpolation coefficients including offset values representing distances from the pixels to corresponding virtual pixels in the corresponding direction where the corresponding virtual pixels have pixel values that are identical to the pixel values of the pixels in the raw input image data absent lateral chromatic aberrations, wherein the corrected versions of the pixel values are included as part of a corrected raw image data; and 
 memory coupled to the correction circuit, the memory configured to store one or more lookup tables that include the plurality of different sets of interpolation coefficients.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. patent application Ser. No. 16/848,131 filed on Apr. 14, 2020, which is incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates to a circuit for processing images and more specifically to a circuit for performing chromatic aberration recovery on images. 
     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. 
     However, image processing pipelines do not account for the use of a wide-angle lens (e.g., a fisheye lens) to generate the image data. When a wide-angle lens is used to generate the image data, the refraction angle of light with different wavelength varies thereby manifesting itself on the image sensor as shifted focal points that are not aligned among red, green, and blue color channels. Thus, color fringing is present at sharp and high contrast edges of full-color images generated from the image data. 
     SUMMARY 
     Embodiments relate to of the present disclosure relate to a circuit for correcting lateral chromatic aberration (LCA) generated by image sensors. In one embodiment, an image processor circuit receives pixel values of pixels of a color in raw input image data. The color may be red or blue, but not green. The image processor circuit generates a first corrected version of the pixel values. The image processor circuit generates the first correction version of the pixel values by performing interpolation of pixel values of a first subset of pixels of the color arranged in a first direction of the raw image input data. The interpolation may be performed using one or more of first interpolation coefficients that correspond to first offset values representing first distances from the pixels to corresponding virtual pixels in the first direction where the virtual pixels have pixel values that are identical to pixel values of the pixels in the raw image absent lateral chromatic aberrations. 
     The image processor circuit generates second corrected versions of the pixel values by performing interpolation of the first corrected versions of the pixel values of second subset of pixels of the color arranged in a second direction perpendicular where the second direction is the horizontal direction. The interpolation may be performed using one or more of second interpolation coefficients that correspond to second offset values that represent second distances from the pixels in the raw image input data to the corresponding virtual pixels in the second direction. The second corrected versions of the pixel values are part of a corrected raw image data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         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. 
         FIGS.  4 A and  4 B  are conceptual diagrams illustrating longitudinal/axial chromatic aberration and lateral/transverse chromatic aberration, according to one embodiment. 
         FIG.  5    is a conceptual diagram illustrating raw image data generated by an image sensor using a wide-angle lens, according to one embodiment. 
         FIG.  6    is a block diagram illustrating a detailed view of a chromatic aberration recovery (CAR) circuit, according to one embodiment. 
         FIGS.  7 A and  7 B  are conceptual diagrams illustrating vertical interpolation and horizontal interpolation of the raw image data, according to one embodiment. 
         FIG.  8    is a diagram illustrating pixel neighbors of a given pixel, according to one embodiment. 
         FIG.  9    is a flowchart illustrating a method of performing chromatic aberration recovery to reduce color fringing of raw 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 lateral chromatic aberration (LCA) recovery of raw image data generated by image sensors. In one embodiment, raw image data may be in a Bayer color filter array (CFA) pattern (hereinafter also referred to as a “Bayer pattern”). A full-color image created from a Bayer pattern that is generated by an image sensor using a wide-angle lens typically has LCA and axial chromatic aberration (ACA). For a wide-angle lens, the refraction angle for light with different wavelengths varies and manifests itself on image sensors as shifted focal points that are misaligned among red, green, and blue color channels and results in color fringing at sharp and high contrast edges in the full color image. A chromatic aberration recovery circuit performs chromatic aberration recovery on raw image data captured with the wide-angle lens to correct the resulting LCA in the full color images. 
     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, California 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. 
       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, highlight recovery, and chromatic aberration 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). Chromatic aberration recovery is performed by chromatic aberration recovery circuit (CAR)  307  and refers to correcting chromatic aberrations in raw image data images resulting from the use of a wide-angle lens to generate the images. 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. 
     Chromatic Aberration Recovery 
     In general, chromatic aberration is caused by the inability of a lens to focus different wavelengths of light (e.g., different colors of light) to the same point.  FIG.  4 A  illustrates an example of longitudinal (e.g., axial) chromatic aberration. As shown in  FIG.  4 A , wide-angle lens  401  refracts light  403  such that different wavelengths of light (e.g., red light, green light, and blue light) are focused at different distances from the wide-angle lens  401  along the optical axis  405 .  FIG.  4 B  illustrates lateral (e.g., transverse) chromatic aberration, according to one embodiment. As shown in  FIG.  4 B , the wide-angle lens  401  refracts light  403  such that the different wavelengths (e.g., red light, green light, and blue light) are focused at different positions on the focal plane  407 . Chromatic aberration due to the usage of the wide-angle lens  401  as described with respect to  FIGS.  4 A and  4 B  manifests itself as color fringing at edges in full color images. 
       FIG.  5    illustrates raw image data generated using light  403  captured by image sensor  202  using the wide-angle lens  401  in one embodiment. As shown in  FIG.  5   , the raw image data is in a Bayer pattern  501 . The Bayer pattern  501  includes alternating rows of red-green pixels and green-blue pixels. Generally, the Bayer pattern  501  includes more green pixels than red or blue pixels due to the human eye being more sensitive to green light than both red light and blue light. 
       FIG.  6    is a block diagram illustrating a detailed view of the chromatic aberration recovery (CAR) circuit  307 , according to one embodiment. The CAR circuit  307  receives raw input image data  601  and generates corrected raw image data  615  by correcting chromatic aberrations. In one embodiment, the raw input image data  601  is a Bayer pattern that is generated by image sensor  202  using a wide-angle lens as described with respect to  FIG.  5   . A full-color image generated from the raw input image data  601  includes chromatic aberrations due to using the wide-angle lens to generate the raw input image data  601  By using the corrected raw image data  615  to generate a full-color image rather than the raw input image data  601 , chromatic aberrations in the full-color image is reduced. While the description herein describes the CAR circuit  307  receiving raw input image data  601 , in other embodiments the CAR circuit  307  may receive RBG image data and generate corrected RBG image data by correcting chromatic aberrations in the RGB image data. 
     In one embodiment, the CAR circuit  307  includes a pixel locater circuit  602 , an offset look-up table (LUT)  603 , an offset interpolator circuit  605 , a vertical phase LUT  607 , a horizontal phase LUT  609 , a vertical correction circuit  611 , and a horizontal correction circuit  613 . In other embodiments, the CAR circuit  307  may have additional or fewer circuits and LUTs than those shown in  FIG.  6   . 
     The pixel locater circuit  602  receives the raw input image data  601 . The pixel locator circuit  602  identifies the location of each pixel in the raw input image data  601 . The identified location of each pixel in the raw input image data  601  is provided to the offset interpolator circuit  605 . Based on the Bayer pattern arrangement, the pixel locater circuit  602  determines the locations of red and blue pixels for correcting chromatic aberrations. 
     In one embodiment, offset LUT  603  stores a grid of pre-calculated horizontal and vertical offset values. A horizontal offset value and a vertical offset value for a certain pixel represent, respectively, a horizontal distance and a vertical distance to a virtual pixel with a pixel value that corresponds to a pixel value of the certain pixel had there not been any chromatic aberrations. The grid includes a plurality of grid points having a plurality of pixel offset values. The pre-calculated offset values in the grid may be associated with optical configurations of a corresponding image sensor  202  (e.g., use of a specific wide-angle lens). Thus, the offset LUT  603  may store different sets of offset values that are each associated with different image sensors  202 . In one or more embodiments, the grid is coarser than the arrangement of pixels of the Bayer pattern  501 . A particular pixel location may be associated with one or more grid points and comprises four pixel offset values: a horizontal pixel offset value for the red pixels, a vertical pixel offset value for the red pixels, a horizontal offset value for the blue pixels, and a vertical offset value for the blue pixels. 
     The offset interpolator circuit  605  is coupled to the pixel locator circuit  602  and receives the identified locations of the pixels from the pixel locator circuit  602 . In one embodiment, the offset interpolator circuit  605  calculates horizontal and vertical offset values for a subset of pixels (e.g., blue and red pixels) included in the raw input image data  601 . Specifically, the offset interpolator circuit  605  calculates the horizontal and vertical offset values of a blue or red pixel by performing interpolation on pre-calculated horizontal and vertical offset values of grid points surrounding the blue or red pixel as described below with reference to  FIG.  8   . That is, for each red or blue pixel in the raw input image data  601 , the offset interpolator circuit  605  calculates a horizontal pixel offset for the red color channel of the pixel, a vertical pixel offset value for the red color channel of the pixel, a horizontal pixel offset for the blue color channel of the pixel, and a vertical pixel offset value for the blue color channel of the pixel. Thus, the offset interpolator circuit  605  does not calculate horizontal and pixel offsets for the green color channel of the pixel. However, in other embodiments, the offset interpolator circuit  605  may also calculate a horizontal pixel offset for the green color channel of the pixel and a vertical pixel offset value for the green color channel of the pixel. Generally, when the horizontal and vertical pixel offset values for two color channels are calculated, the horizontal and vertical pixel offset values for the remaining color channel (RGB) are not calculated. 
       FIG.  7 A  illustrates vertical offset pixel correction for a blue color channel of pixel  701  included in the raw input image data  601 . Due to chromatic aberrations in the vertical direction, the pixel value of blue pixel P2 captured by the image sensor  202  (and representing in the Bayer pattern  501 ) is inaccurate. Rather, the pixel value of blue pixel P2 is obtained from a virtual pixel  701  vertically offset by a distance  703  indicated by an arrow shown in  FIG.  7 A  (assuming that there is no horizontal shifting of focal point due to chromatic aberrations). As will be further described below, the vertical pixel offset  703  indicated by the arrow is used as a parameter to interpolate the pixel value at virtual pixel location  701  using pixel values of neighboring blue pixels P0, P1, P2, and P3 in the vertical direction. The pixel value of the virtual pixel location  701  then replaces the pixel value of blue pixel P2 as a corrected pixel value. Such replacement of pixel values is performed for all blue pixels to account for the vertical chromatic aberration. The red color channel of pixels also have their vertical offset corrected in a similar manner as the blue color channel of pixels shown in  FIG.  7 A . 
       FIG.  7 B  illustrates horizontal pixel offset correction for the blue color channel of pixel  701  included in the raw input image data  601 . The blue pixels in  FIG.  7 B  have pixel values corrected using vertical offsets as explained above with reference to  FIG.  7 A . As shown in  FIG.  7 B , the pixel value of blue pixel P6 which has the vertical chromatic aberration does not take into account the horizontal chromatic aberration. In order to account for the horizontal chromatic aberration, the pixel value of pixel P6 is replaced with a pixel value of a virtual pixel  711  that is horizontally offset from pixel P6 by a distance  713 . As will be further described below, the horizontal pixel offset  713  is used as a parameter to interpolate pixel values of neighboring pixels P4, P5, P6, and P7 in the horizontal direction. Such replacement is performed across all blue pixels to correct the horizontal chromatic aberration. The red color channel of pixels also have their horizontal offset corrected in a similar manner as the blue color channel of pixels shown in  FIG.  7 B . 
       FIG.  8    illustrates grid points GP0 through GP4 that surrounds a given pixel  801 , in one embodiment. As described above, each of grid points GP0 through GP4 has an associated vertical and horizontal offset values for red and blue pixels stored in offset LUT  603 . If pixel  801  is a red pixel, the offset interpolator circuit  605  performs a bilateral interpolation on four vertical offset values of the four grid points GP0 through GP4 for red pixels and generates an interpolated vertical offset value  615  for the red pixel. The offset interpolator circuit  605  also performs a bilateral interpolation on four horizontal offset values of the four grid points GP0 through GP3 for red pixels and generates an interpolated horizontal offset value  617  for the red pixel. If pixel  801  is a blue pixel, the offset interpolator circuit  605  performs a bilateral interpolation on four vertical offset values of the four grid points GP0 through GP3 for blue pixels and generates an interpolated vertical offset value  615  for blue red pixel, and performs a bilateral interpolation on four horizontal offset values of the four grid points GP0 through GP3 for blue pixels and generates an interpolated horizontal offset value  617  for the blue pixel. 
     Referring back to  FIG.  6   , the offset interpolator circuit  605  provides the vertical pixel offset values  615  for the red and blue color channels of each pixel in the raw input image data  601  to the vertical phase LUT  607  and provides the horizontal pixel offset values  617  for the red and blue color channels to the horizontal phase LUT  609 . In one embodiment, the vertical phase LUT  607  stores a table of interpolation coefficients (e.g., spline interpolator coefficients) for a plurality of phases in the vertical direction where each phase has a set of coefficients C0, C1, C2, and C3. Similarly, the horizontal phase LUT  609  stores a table of interpolation coefficients (e.g., spline interpolation coefficients) for a plurality of phases in the horizontal direction where each phase has a set of coefficients C0, C1, C2, and C3. Each table of interpolator coefficients is pre-computed and is associated with the same wide-angle lens that is associated with the offset LUT  603 . 
     The vertical phase LUT  607  uses the vertical pixel offsets calculated for the red and blue color channels for each pixel to define the phase of bilinear interpolation in the vertical direction. Similarly, the horizontal phase LUT  609  uses the horizontal offsets calculated for the red and blue color channels for each pixel to define the phase of bilinear interpolation in the horizontal direction. The phase in each of the vertical and horizontal directions functions as an index to its respective set of coefficients in the respective phase LUT. 
     The vertical phase LUT  607  identifies the set of coefficient values that are associated with the vertical pixel offset values for the red color channel and the set of coefficient values that are associated with the vertical pixel offset values for the blue color channels and provides the identified sets of coefficient values to the vertical correction circuit  611 . Similarly, the horizontal phase LUT  509  identifies the set of coefficients that are associated with the horizontal pixel offset value for the red color channel and the set of coefficient values that are associated with the horizontal pixel offset value for the blue color channel and provides the identified sets of coefficients to the horizontal correction circuit  611 . 
     The vertical correction circuit  611  calculates blue and red pixel values with chromatic aberrations corrected in the vertical direction relative to raw input image data  601 . No change is made to the green pixel values. In one embodiment, the vertical correction circuit  611  calculates vertically corrected versions of the red pixel values (P v ) and the vertically corrected versions of the blue pixel values (P v ) using spline interpolation. One example of the spline function is as follows:
 
 P   v =½(( u   2 (2− u )− u ))· p   n−1 +( u   2 (3 u −5)+2))· p   n +( u   2 (4−3 u )+ u ))· p   n+1 +( u   2 ( u− 1)))· p   n+2   equation (1)
 
 P   v   =C   0   P   0   +C   1   P   1   +C   2   P   2   +C   3   P   3   equation (2)
 
where u represents a vertical pixel offset values, P 0  through P 3  represent pixel values of four blue or red pixels in the same column and closest to a virtual pixel corresponding to the blue or red pixel whose value is being corrected to account for chromatic aberrations, and C 0  through C 3  are the interpolation coefficients. Note that the usage of the spline function is just one example for correcting chromatic aberration. In other embodiments, different functions may be used for performing image sharpening or image smoothing, or any combination of thereof with chromatic aberration correction.
 
     To calculate the vertically corrected version of a pixel value for a red pixel, the vertical correction circuit  611  obtains the pixel offset values  615  from offset interpolator circuit  605 , retrieves a set of coefficients C 0 , C 1 , C 2 , and C3 corresponding to the pixel offset value  615  from the vertical phase LUT  607 . Using the pixel offset values of the pixel&#39;s neighbors and the set of coefficients, the vertical correction unit  611  calculates the pixel correction value of the red color channel for each pixel using equation (2). 
     To calculate the vertically corrected version of a pixel value for a blue color, the vertical correction circuit  611  obtains the pixel offset values  615  from offset interpolator circuit  605 , retrieves a set of coefficients C 0 , C 1 , C 2 , and C3 corresponding to the pixel offset value  615  from the vertical phase LUT  607 . Using the pixel offset values of the pixel&#39;s neighbors and the set of coefficients, the vertical correction unit  611  calculates the pixel correction value of the blue color channel for each pixel using equation (2). 
     The horizontal correction circuit  613  calculates blue and red pixel values with chromatic aberrations corrected in the horizontal direction relative to raw input image data  601 . No change is made to the green pixel values. In one embodiment, the horizontal correction circuit  613  calculates horizontally correction versions of the red pixel values (P h ) and vertically correction versions of the blue pixel values (P h ) using spline interpolation. One example of the spline function is as follows:
 
 P   h =½(( v   2 (2− v )− v ))· p   n−1 +( v   2 (3 v− 5)+2))· p   n +( v   2 (4−3 v )+ v ))· p   n+1 +( v   2 ( v− 1)))· p   n+2   equation (3)
 
 P   y   =C   4   P   4   +C   5   P   5   +C   6   P   6   +C   7   P   7   equation (4)
 
where v represents a horizontal pixel offset value, P 4  through P 7  represent pixel values of four blue or red pixels in the same column and closest to a virtual pixel corresponding to the blue or red pixel whose value is being corrected to account for chromatic aberrations, and C 4  through C 7  are the interpolation coefficients.
 
     To calculate the horizontally corrected version of a pixel value for a red pixel, the horizontal correction circuit  613  obtains the pixel offset values  617  from offset interpolator circuit  605 , retrieves a set of coefficients C 4 , C 5 , C 6 , and C7 corresponding to the pixel offset value  617  from the horizontal phase LUT  609 . Using the pixel offset values of the pixel&#39;s neighbors and the set of coefficients, the horizontal correction unit  613  calculates the pixel correction value of the red color channel for each pixel using equation 4. 
     To calculate the horizontally corrected version of a pixel value for a blue pixel, the horizontal correction circuit  613  obtains the pixel offset values  617  from offset interpolator circuit  605 , retrieves a set of coefficients C 4 , C 5 , C 6 , and C7 corresponding to the pixel offset value  617  from the horizontal phase LUT  609 . Using the pixel offset values of the pixel&#39;s neighbors and the set of coefficients, the horizontal correction unit  613  calculates the pixel correction value of the blue color channel for each pixel using equation 4. 
     The horizontal and vertical pixel correction values for the blue color channel and the horizontal and vertical pixel correction values for the red color channel of each pixel from the raw input image data  601  represent the corrected raw image data  615  shown in  FIG.  6   . The corrected raw image data  615  can be used by the image signal processor  206  to generate a full-color image with reduced chromatic aberrations. 
       FIG.  9    is a flowchart illustrating a method of performing chromatic aberration recovery to reduce color fringing of raw 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. 
     In one embodiment, CAR circuit  307  receives  901  pixel values of pixels of a color in raw input image data. The color may be red or blue, but not green. The CAR circuit  307  generates  903  first corrected version of the pixel values. In one embodiment, the CAR circuit  307  generates the first correction version of the pixel values by performing interpolation of pixel values of a first subset of pixels of the color arranged in a first direction of the raw image input data where the first direction is the vertical direction. The interpolation may be performed using one or more of first interpolation coefficients that correspond to first offset values representing first distances from the pixels to corresponding virtual pixels in the first direction where the virtual pixels have pixel values that are identical to pixel values of the pixels in the raw image absent lateral chromatic aberrations. 
     The CAR circuit  307  generates second corrected versions of the pixel values by performing interpolation of the first corrected versions of the pixel values of second subset of pixels of the color arranged in a second direction perpendicular where the second direction is the horizontal direction. The interpolation may be performed using one or more of second interpolation coefficients that correspond to second offset values that represent second distances from the pixels in the raw image input data to the corresponding virtual pixels in the second direction. The second corrected versions of the pixel values are part of a corrected raw image data. 
     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: 20220502
Publication Date: 20240102
Grant Date: 20240102
Priority Date: 20200414
Inventors: WU, CHIHSIN
POPE, DAVID R.
Assignee: APPLE INC
CPC Classifications: [{"code": "H04N25/611", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/006", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N9/646", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N25/68", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10024", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N25/61", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N25/611", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N25/611", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/10024", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N9/646", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N25/68", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/80", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N25/68", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 78005616