PATENT DOCUMENT

Publication Number: US-11024006-B2
Application Number: US-201916391224-A
Country: US
Kind Code: B2

Title: Tagging clipped pixels for pyramid processing in image signal processor

Abstract:
A portable electronic device may include an image signal processor that includes a clipping circuit, a pyramid generator circuit, and an image fusion processor. The clipping circuit clips pixel values that are under-exposed or over-exposed. The pyramid generator circuit applies a filter to the pixels of the image to generate a filtered image. Some of the filtered pixels may be generated from one or more clipped pixel values. The pyramid generator circuit identifies those filtered pixels that are generated from one or more clipped pixel values and marks the identified filtered pixels with a tag. The pyramid generator circuit decimates the filtered image to generate a downscaled image, which may include one or more filtered pixels that are marked with the tags. The image fusion processor fuses the downscaled image with another image. The pixels that are marked with the tags may be disregarded in the fusion process.

Claims:
What is claimed is: 
     
       1. An image signal processor, comprising:
 a clipping circuit configured to:
 identify one or more pixel values in an image that are beyond a predetermined range; and 
 replace the one or more identified pixel values with one or more clipped pixel values; and 
 
 a pyramid generator circuit coupled to the clipped circuit and configured to:
 apply one or more image filters to the image to generate a filtered image that comprises filtered pixels; 
 identify one or more of the filtered pixels that are generated from the one or more clipped pixel values; 
 mark the identified filtered pixels with a tag indicating that the identified filtered pixels are generated from the one or more clipped pixel values; and 
 decimate the filtered image to generate a downscaled image having a reduced number of pixels compared to the image, the downscaled image comprising one or more pixels marked with the tag, wherein the downscaled image is configured to be fused with a second image to generate a fused image, wherein the filtered pixels in the downscaled image marked with the tag are disregarded in the fusing. 
 
 
     
     
       2. The image signal processor of  claim 1 , further comprising:
 a fusion circuit coupled to the pyramid generator circuit and configured to:
 receive the downscaled image from the pyramid generator circuit and a second image, the downscaled image from the pyramid generator circuit and the second image capturing a same scene with different exposure times; and 
 fuse the downscaled image with the second image to generate the fused image. 
 
 
     
     
       3. The image signal processor of  claim 2 , wherein the fusion circuit is further configured to:
 receive a first unsealed image generated from the image; 
 receive a second unsealed image, the second unsealed image being an unsealed version of the second image that is downscaled; and 
 fuse the first and second unsealed images to generate a second fuse image. 
 
     
     
       4. The image signal processor of  claim 1 , further comprising:
 a warping circuit coupled to the pyramid generator circuit and configured to:
 interpolate pixel values in the image to generate an interpolated image; 
 identify one or more interpolated pixel values that are generated from the one or more clipped pixel values; 
 mark the identified interpolated pixel values with the tag; and 
 decimate the interpolated image. 
 
 
     
     
       5. The image signal processor of  claim 1 , wherein the one or more image filters comprising a horizontal finite impulse response (FIR) filter and a vertical FIR filter and one of the filtered pixels is a weighted average of a subset of pixel values in the image. 
     
     
       6. The image signal processor of  claim 1 , wherein identify one or more of the filtered pixels that are generated from the one or more clipped pixel values comprises:
 generate one of the filtered pixels from a subset of pixel values of the image; 
 determine that one of the pixel values in the subset has a boundary value of the predetermined range; and 
 identify that the one of the filtered pixels is generated from the one or more clipped pixel values responsive to one of the pixel values in the subset has the boundary value. 
 
     
     
       7. The image signal processor of  claim 1 , wherein the one or more image filters comprises an erosion mask, and wherein a number of filtered pixels marked with the tag in the filtered image is larger than a number of pixels in the image associated with clipped pixel values. 
     
     
       8. The image signal processor of  claim 1 , wherein a support size of at least one of the image filters is selectable by the pyramid generator circuit. 
     
     
       9. The image signal processor of  claim 1 , wherein the one or more filters comprises a luminance filter and a chrominance filter that is different from the luminance filter. 
     
     
       10. The image signal processor of  claim 1 , wherein the pyramid generator circuit is further configured to generate an unscaled image in addition to the downscaled image, the unsealed image having a same number of pixels of the image and having luminance values only. 
     
     
       11. A method of operating an image signal processor comprising a pyramid generator circuit, the method comprising:
 identifying one or more pixel values in an image that are beyond a predetermined range; 
 replacing the one or more identified pixel values with one or more clipped pixel values; 
 applying one or more image filters to the image to generate a filtered image that comprises filtered pixels; 
 identifying one or more of the filtered pixels that are generated from the one or more clipped pixel values; 
 marking the identified filtered pixels with a tag indicating that the identified filtered pixels are generated from the one or more clipped pixel values; and 
 decimating the filtered image to generate a downscaled image having a reduced number of pixels compared to the image, the downscaled image comprising one or more pixels marked with the tag, wherein the downscaled image is configured to be fused with a second image to generate a fused image, wherein the filtered pixels in the downscaled image marked with the tag are disregarded in the fusing. 
 
     
     
       12. The method of  claim 11 , further comprising:
 receiving a second image different from the downscaled image, the downscaled image and the second image capturing a same scene with different exposure times; and 
 fusing the downscaled image with the second image to generate the fused image. 
 
     
     
       13. The method of  claim 12 , wherein fusing the downscaled image with the second image to generate the fused image comprises:
 for a first fused pixel of the fused image that corresponds to a first filtered pixel in the downscaled image that is not marked with the tag, determining a first weighted average of the first filtered pixel in the downscaled image and a first corresponding pixel in the second image; and 
 for a second fused pixel of the fused image that corresponds to a second filtered pixel in the downscaled image that is marked with the tag, using a second corresponding pixel in the second image as the second fused pixel and disregarding the second filtered pixel. 
 
     
     
       14. The method of  claim 11 , further comprising:
 interpolating pixel values in the image to generate an interpolated image; 
 identifying one or more interpolated pixel values that are generated from the one or more clipped pixel values; 
 marking the identified interpolated pixel values with the tag; and 
 decimating the interpolated image. 
 
     
     
       15. The method of  claim 11 , wherein identifying one or more of the filtered pixels that are generated from the one or more clipped pixel values comprises:
 generating one of the filtered pixels from a subset of pixel values of the image; 
 determining that one of the pixel values in the subset has a boundary value of the predetermined range; and 
 identifying that the one of the filtered pixels is generated from the one or more clipped pixel values responsive to one of the pixel values in the subset has the boundary value. 
 
     
     
       16. The method of  claim 11 , wherein the one or more image filters comprises an erosion mask, and wherein a number of filtered pixels marked with the tag in the filtered image is larger than a number of pixels in the image associated with clipped pixel values. 
     
     
       17. An electronic device, comprising:
 an image sensor system configured to capture a plurality of images of a same scene, the plurality of images comprising a first image and a second image, the first image corresponding to a first exposure time and the second image corresponding to a second exposure time shorter than the first exposure time; and 
 an image signal processor configured to:
 identify one or more pixel values in the first image that are beyond a predetermined range; 
 replace the one or more identified pixel values with one or more clipped pixel values; 
 apply one or more image filters to the first image to generate a filtered image that comprises filtered pixels; 
 identify one or more of the filtered pixels that are generated from the one or more clipped pixel values; 
 mark the identified filtered pixels with a tag indicating that the identified filtered pixels are generated from the one or more clipped pixel values; and 
 decimate the filtered image to generate a first downscaled image having a reduced number of pixels compared to the first image, the first downscaled image comprising one or more pixels marked with the tag, wherein the downscaled image is configured to be fused with a second image to generate a fused image, wherein the filtered pixels in the downscaled image marked with the tag are disregarded in the fusing. 
 
 
     
     
       18. The electronic device of  claim 17 , wherein the image signal processor is further configured to:
 downscale the second image to generate a second downscaled image; and 
 fuse the first downscaled image with the second downscaled image to generate the fused image. 
 
     
     
       19. The electronic device of  claim 18 , wherein the image signal processor is further configured to:
 generate a first unsealed image from the first image, the first unsealed image including only luminance values of the first image; 
 generate a second unsealed image from the second image, the second unsealed image including only luminance values of the second image; and 
 fuse the first and second unsealed images to generate a second fused image different from the fused image. 
 
     
     
       20. The electronic device of  claim 19 , wherein the image signal processor is further configured to:
 generate a high dynamic range image by combining the first fused image and the second fused image, the high dynamic range image having a higher contrast than the first image and than the second image.

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates to a circuit for processing images and more specifically to generating images with downscaled resolutions. 
     2. Description of the Related Art 
     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 a 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. 
     In generating a high dynamic range image, an image sensor system may capture different images with different exposure times. Depending on the image conditions, some of the pixels in the captured images may be under-exposed or over-exposed. The exposure of an image could affect the perceived quality of an image. 
     SUMMARY 
     Embodiments relate to an image signal processor that includes a clipping circuit and a pyramid generator circuit. The clipping circuit identifies one or more pixel values in an image that are beyond a predetermined range. The clipping circuit replaces the one or more pixel values with one or more clipped pixel values. The pyramid generator circuit applies one or more image filters to the image to generate a filtered image that includes filtered pixels. The pyramid generator circuit also identifies one or more of the filtered pixels that are generated from the one or more clipped pixel values. The pyramid generator circuit marks the identified filtered pixels with a tag. The tag indicates that the identified filtered pixels are generated from the one or more clipped pixel values. The pyramid generator circuit further decimates the filtered image to generate a downscaled image. The downscaled image has a reduced number of pixels compared to the image. The downscaled image includes one or more pixels that are marked with the tag. 
    
    
     
       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. 
         FIG. 4  is a block diagram illustrating a portion of the image processing pipeline including circuitry for image fusion, according to one embodiment. 
         FIG. 5  is a block diagram illustrating a portion of a pyramid generator and image fusion pipeline, according to one embodiment. 
         FIG. 6  is a series of conceptual diagrams illustrating a filtering and downscaling process in a pyramid generator circuit, according to one embodiment. 
         FIG. 7  is a series of conceptual diagrams illustrating a resampling process in a warping circuit, according to one embodiment. 
         FIG. 8  is a flowchart illustrating a method of image fusion processing, according to one embodiment. 
     
    
    
     The figures depict, and the detailed 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 relate to a pyramid generator circuit of an image signal processor that marks clipped over-exposed or under-exposed pixels when a downscaled image is generated. A pyramid generator circuit may generate a series of pyramid images, which could include an unscaled image and a downscaled image, both generated from an input image. An image received by the pyramid generator circuit may include one or more clipped pixel values, which may be pixel values that are clipped from over-exposed or under-exposed pixels. In generating a downscaled image, the pyramid generator circuit applies a filter to the image to generate a filtered image. A filtered pixel in the filtered image is generated from a subset of pixels in the input image. Some of the filtered pixels may be generated from one or more clipped pixel values. The pyramid generator circuit identifies those filtered pixels that are generated from one or more clipped pixel values and marks the identified filtered pixels with a tag. The pyramid generator circuit downscales the filtered image to generate a downscaled image, which may include one or more filtered pixels that are marked with the tags. In a fusion process downstream of the pyramid generator circuit, the downscaled image is fused with another image. The pixels that are marked with the tags may be disregarded in the fusion process. 
     Exemplary Electronic Device 
     Embodiments of electronic devices, user interfaces for such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as personal digital assistant (PDA) and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, Apple Watch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable electronic devices, such as wearables, laptops or tablet computers, are optionally used. In some embodiments, the device is not a portable communications device, but is a desktop computer or other computing device that is not designed for portable use. In some embodiments, the disclosed electronic device may include a touch sensitive surface (e.g., a touch screen display and/or a touch pad). An example electronic device described below in conjunction with  FIG. 1  (e.g., device  100 ) may include a touch-sensitive surface for receiving user input. The electronic device may also include one or more other physical user-interface devices, such as a physical keyboard, a mouse and/or a joystick. 
     Figure (FIG.)  1  is a high-level diagram of an electronic device  100 , according to one embodiment. Device  100  may include one or more physical buttons, such as a “home” or menu button  104 . Menu button  104  is, for example, used to navigate to any application in a set of applications that are executed on device  100 . In some embodiments, menu button  104  includes a fingerprint sensor that identifies a fingerprint on menu button  104 . The fingerprint sensor may be used to determine whether a finger on menu button  104  has a fingerprint that matches a fingerprint stored for unlocking device  100 . Alternatively, in some embodiments, menu button  104  is implemented as a soft key in a graphical user interface (GUI) displayed on a touch screen. 
     In some embodiments, device  100  includes touch screen  150 , menu button  104 , push button  106  for powering the device on/off and locking the device, volume adjustment buttons  108 , Subscriber Identity Module (SIM) card slot  110 , head set jack  112 , and docking/charging external port  124 . Push button  106  may be used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  100  also accepts verbal input for activation or deactivation of some functions through microphone  113 . The device  100  includes various components including, but not limited to, a memory (which may include one or more computer readable storage mediums), a memory controller, one or more central processing units (CPUs), a peripherals interface, an RF circuitry, an audio circuitry, speaker  111 , microphone  113 , input/output (I/O) subsystem, and other input or control devices. Device  100  may include one or more image sensors  164 , one or more proximity sensors  166 , and one or more accelerometers  168 . Device  100  may include more than one type of image sensors  164 . Each type may include more than one image sensor  164 . For example, one type of image sensors  164  may be cameras and another type of image sensors  164  may be infrared sensors that may be used for face recognition. In addition 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 lens position 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, a 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  228  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 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 share the same circuit board that controls the mechanical components of the image sensors (e.g., actuators that change the lens positions 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 a hardware circuit that analyzes raw image data to determine an appropriate lens position of each image sensor  202 . In one embodiment, the raw image data may include data that is transmitted from image sensing pixels that specialize 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 lens position. 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 lens positions 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 lens position 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 lens position 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 seamlessly transition from image data captured by one image sensor  202  to image data captured by another image sensor  202  without waiting for the second image sensor  202  to adjust its lens position because two or more auto-focus circuits  350  may continuously provide auto-focus data to the image sensor system  201 . 
     Raw image data captured by different image sensors  202  may also be transmitted to sensor interface  302 . Sensor interface  302  receives raw image data from image sensor  202  and processes the raw image data into an image data processable by other stages in the pipeline. Sensor interface  302  may perform various preprocessing operations, such as image cropping, binning or scaling to reduce image data size. In some embodiments, pixels are sent from the image sensor  202  to sensor interface  302  in raster order (e.g., horizontally, line by line). The subsequent processes in the pipeline may also be performed in raster order and the result may also be output in raster order. Although only a single image sensor and a single sensor interface  302  are illustrated in  FIG. 3 , when more than one image sensor is provided in device  100 , a corresponding number of sensor interfaces may be provided in ISP  206  to process raw image data from each image sensor. 
     Front-end pipeline stages  330  process image data in raw or full-color domains. Front-end pipeline stages  330  may include, but are not limited to, raw processing stage  306  and resample processing stage  308 . A raw image data may be in Bayer raw format, for example. In Bayer raw image format, pixel data with values specific to a particular color (instead of all colors) is provided in each pixel. In an image capturing sensor, image data is typically provided in a Bayer pattern. Raw processing stage  306  may process image data in a Bayer raw format. 
     The operations performed by raw processing stage  306  include, but are not limited, sensor linearization, black level compensation, fixed pattern noise reduction, defective pixel correction, raw noise filtering, lens shading correction, white balance gain, and highlight recovery. Sensor linearization refers to mapping non-linear image data to linear space for other processing. Black level compensation refers to providing digital gain, offset and clip independently for each color component (e.g., Gr, R, B, Gb) of the image data. Fixed pattern noise reduction refers to removing offset fixed pattern noise and gain fixed pattern noise by subtracting a dark frame from an input image and multiplying different gains to pixels. Defective pixel correction refers to detecting defective pixels, and then replacing defective pixel values. Raw noise filtering refers to reducing noise of image data by averaging neighbor pixels that are similar in brightness. Highlight recovery refers to estimating pixel values for those pixels that are clipped (or nearly clipped) from other channels. Lens shading correction refers to applying a gain per pixel to compensate for a dropoff in intensity roughly proportional to a distance from a lens optical center. White balance gain refers to providing digital gains for white balance, offset and clip independently for all color components (e.g., Gr, R, B, Gb in Bayer format). Components of ISP  206  may convert raw image data into image data in full-color domain, and thus, raw processing stage  306  may process image data in the full-color domain in addition to or instead of raw image data. 
     Resample processing stage  308  performs various operations to convert, resample, or scale image data received from raw processing stage  306 . Operations performed by resample processing stage  308  may include, but not limited to, demosaic operation, per-pixel color correction operation, Gamma mapping operation, color space conversion and downscaling or sub-band splitting. Demosaic operation refers to converting or interpolating missing color samples from raw image data (for example, in a Bayer pattern) to output image data into a full-color domain. Demosaic operation may include low pass directional filtering on the interpolated samples to obtain full-color pixels. Per-pixel color correction operation refers to a process of performing color correction on a per-pixel basis using information about relative noise standard deviations of each color channel to correct color without amplifying noise in the image data. Gamma mapping refers to converting image data from input image data values to output data values to perform gamma correction. For the purpose of Gamma mapping, lookup tables (or other structures that index pixel values to another value) for different color components or channels of each pixel (e.g., a separate lookup table for R, G, and B color components) may be used. Color space conversion refers to converting color space of an input image data into a different format. In one embodiment, resample processing stage  308  converts RGB 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, 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 statistical 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 noise 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 a 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 an input image data and output image data in order to correct artifacts and motion caused by sensor motion during the capture of the image frame. Output rescale may provide image data via output interface  316  to various other components of device  100 , as discussed above with regard to  FIGS. 1 and 2 . 
     In various embodiments, the functionally of components  302  through  350  may be performed in a different order than the order implied by the order of these functional units in the image processing pipeline illustrated in  FIG. 3 , or may be performed by different functional components than those illustrated in  FIG. 3 . Moreover, the various components as described in  FIG. 3  may be embodied in various combinations of hardware, firmware or software. 
     Example Pipelines for Image Fusion 
       FIG. 4  is a block diagram illustrating a portion of the image processing pipeline including circuitry for image fusion, according to one embodiment. Images  402 ,  404  are captured by image sensor system  201  and passed onto vision module  322 . In one embodiment, image  402  is captured shortly before or after capturing image  404 . Alternatively, images  402  and  404  are captured at the same time using two different image sensors  202  with different exposure times. Image  402  captures a scene with a first exposure time, and image  404  captures the same scene with a second exposure time that may be different than the first exposure time. If the second exposure time is shorter than the first exposure time, image  402  can be referred to as “long exposure image” and image  404  can be referred to as “short exposure image.” Each image  402 ,  404  includes multiple color components, e.g., luma and chroma color components. Image  402  is passed onto feature extractor circuit  406  of vision module  322  for processing and feature extraction. Image  404  may be passed onto feature extractor circuit  410  of vision module  322  for processing and feature extraction. Alternatively, feature extractor circuit  410  may be turned off. 
     Feature extractor circuit  406  extracts first keypoint information  408  about first keypoints (e.g., salient points) in image  402  by processing pixel values of pixels in image  402 . The first keypoints are related to certain distinguishable features (also referred to “salient points”) in image  402 . Extracted first keypoint information  408  can include information about spatial locations (e.g., coordinates) of at least a subset of pixels in image  402  associated with the first keypoints of image  402 . For each of the first keypoints in image  402 , feature extractor circuit  406  may also extract and encode a keypoint descriptor, which includes a keypoint scale and orientation information. Thus, first keypoint information  408  extracted by feature extractor circuit  406  may include information about a spatial location of each of the first keypoints of image  402  and a keypoint descriptor of each of the first keypoints of image  402 . First keypoint information  408  associated with at least the subset of pixels of image  402  is passed onto CPU  208  for processing. 
     Feature extractor circuit  410  extracts second keypoint information  412  about second keypoints in image  404  by processing pixel values of pixels in image  404 . The second keypoints are related to certain distinguishable features (e.g., salient points) in image  404 . Extracted second keypoint information  412  can include information about spatial locations (e.g., coordinates) of at least a subset of pixels in image  404  associated with the second keypoints of image  404 . For each of the second keypoints in image  404 , feature extractor circuit  410  may also extract and encode a keypoint descriptor, which includes a keypoint scale and orientation information. Thus, second keypoint information  412  extracted by feature extractor circuit  410  may include information about a spatial location of each of the second keypoints of image  404  and a keypoint descriptor of each of the second keypoints of image  404 . Second keypoint information  412  associated with at least the subset of pixels of image  404  are passed onto CPU  208  for processing. Alternatively (not shown in  FIG. 4 ), feature extractor circuit  410  is turned off. In such case, second keypoints of image  404  are not extracted and only first keypoint information  408  is passed onto CPU  208  for processing. 
     CPU  208  builds a model describing correspondence between image  402  and image  404 . CPU  208  searches for correspondences between first keypoint information  408  of image  402  and second keypoint information  412  of image  404  to generate at least one motion vector representing relative movement in image  402  and image  404 . In one embodiment, CPU  208  correlates (matches) first keypoint information  408  with second keypoint information  412 , e.g., by comparing and pairing keypoint descriptors extracted from images  402  and  404  to determine a set of keypoint information matches (e.g., pairs of keypoint descriptors extracted from images  402  and  404 ). CPU  208  then performs a model fitting algorithm by processing the determined set of keypoint information matches to build the model. The model fitting algorithm may be designed to discard false matches during the model building process. The model fitting algorithm may be based on, e.g., the iterative random sample consensus (RANSAC) algorithm. The model built by CPU  208  includes information about mapping between pixels in the images  402  and  404 . The model may represent a linear, affine and perspective transformation. Alternatively, the model may be a non-linear transformation. Based on the model, warping parameters (mapping information)  418  may be generated by CPU  208  and sent to warping circuit  428  for spatial transformation of image  402  and/or image  404 . Warping parameters  418  can be used in a form of a matrix for spatial transformation (e.g., warping) of image  402  and/or image  404 . The matrix for spatial transformation represents a geometric transformation matrix or a mesh grid with motion vectors defined for every grid point. Alternatively, a dedicated circuit instead of CPU  208  may be provided to perform the RANSAC algorithm and to generate warping parameters  418 . 
     In the embodiment when feature extractor circuit  410  is turned off and only first keypoint information  408  is passed onto CPU  208 , CPU  208  generates a motion vector for each of the first keypoints of image  402 . This is done by performing, e.g., the NCC search within an expected and configurable displacement range to determine a best feature match within a defined spatial vicinity (patch) of each first keypoint of image  402 . In such case, CPU  208  performs a model fitting algorithm (e.g., the RANSAC algorithm) that uses first keypoint information  408  (e.g., coordinates of the first keypoints) and corresponding motion vectors determined based on feature matches to build a model, whereas matching of keypoints between images  402  and  404  is not performed. The model fitting algorithm may be designed to discard false feature matches. Based on the built model, CPU  208  generates warping parameters (mapping information)  418  that is sent to warping circuit  428  for spatial transformation of image  402 . Alternatively, a dedicated circuit instead of CPU  208  may be provided to perform the NCC search and to generate a motion vector for each of the first keypoints of image  402 . In such case, CPU  208  uses the motion vector for each of the first keypoints generated by the dedicated circuit to build the model. 
     Image  402 , which may be a long exposure image, is also passed onto image enhancement processor  420  that performs certain processing of image  402 , e.g., noise removal, enhancement, etc., to obtain processed version  422  of image  402 . Processed version  422  is passed onto clipping marker circuit  424 . Clipping marker circuit  424  identifies clipped (e.g., oversaturated) pixels in processed version  422  of image  402  having one or more color component values that exceed threshold values as clipping markers. Clipping marker circuit  424  may replace the pixel values with predetermined pixel values so that any of these pixels or any other pixel derived from these pixels downstream from clipping marker circuit  424  can be identified and addressed appropriately in subsequent processing, such as corresponding morphological operations (e.g., erosion or dilation) of the clipping markers. For example, the morphological operations can be conducted during a warping operation performed by warping circuit  428 , during a pyramid generation performed by pyramid generator circuit  432 , and/or during a fusion operation performed by image fusion processor  444 . 
     Warping circuit  428  accommodates the linear and non-linear transformations defined by the model generated by CPU  208 . Warping circuit  428  warps processed image  426  using the mapping information according to the warping parameters  418  to generate warped version  430  of image  402  (e.g., warped image  430 ) spatially more aligned to image  404  than to image  402 . Alternatively (not shown in  FIG. 4 ), warping circuit  428  warps image  404  using the mapping information in the model to generate warped version  430  of image  404  spatially more aligned to image  402  than to image  404 . Warped image  430  generated by warping circuit  428  is then passed onto pyramid generator circuit  432 . The warping circuit  428  may also perform image resampling to scale the image by a fractional value. 
     Pyramid generator circuit  432  generates multiple downscaled warped images each having a different resolution by sequentially downscaling warped image  430 . Each downscaled warped image includes multiple color components. The downscaled warped images obtained from warped image  430  may be stored in e.g., system memory  230  (not shown in  FIG. 4 ). Low frequency components of the downscaled warped images and a low frequency component of an unscaled single color version (e.g., luma component) of warped image  430  are passed as warped image data  434  onto image fusion processing circuit  444  for fusion with corresponding image data  442  obtained from image  404 . The operation of the pyramid generator circuit will be discussed in further details in  FIG. 5  through  FIG. 7 . Note that in some embodiments, image enhancement processor  420 , clipping locator circuit  424 , warping circuit  428 , and pyramid generator circuit  432  are part of noise processing stage  310 . In some embodiments, one or more of the image enhancement processor  420 , clipping locator circuit  424 , warping circuit  428 , and pyramid generator circuit  432  are outside of noise processing stage  310 , such as in another stage of back-end pipeline stages  340 . 
     Image enhancement processor  436  performs certain processing of image  404  (e.g., noise removal, enhancement, etc.) to obtain processed image  438  for passing onto pyramid generator circuit  440 . Pyramid generator circuit  440  generates multiple downscaled images each having a different resolution by sequentially downscaling processed image  438 . Each downscaled image generated by pyramid generator circuit  440  includes the multiple color components (e.g., luma and chroma components). The downscaled images obtained from processed image  438  may be stored in, e.g., system memory  230 . Low frequency components of the downscaled images and a low frequency component of an unscaled single color version (e.g., luma component) of processed image  438  are passed onto image fusion processing circuit  444  as image data  442 . Note that image enhancement processor  436  and pyramid generator circuit  440  may also be part of noise processing stage  310 . 
     Image fusion processing circuit  444  performs per-pixel blending between a portion of warped image data  434  related to the unscaled single color version of warped image  430  with a portion of image data  442  related to the unscaled single color version of processed image  438  to generate an unscaled single color version of fused image  446 . Image fusion processing circuit  444  also performs per-pixel blending between a portion of warped image data  434  related to a downscaled warped image (obtained by downscaling warped image  430 ) and a portion of image data  442  related to a corresponding downscaled image (obtained by downscaling processed image  438 ) to generate first downscaled version  448  of the fused image comprising the multiple color components. First downscaled version  448  has a pixel resolution equal to a quarter of a pixel resolution of unscaled single color version  446 . Unscaled single color version  446  and first downscaled version  448  are passed onto post-processing circuit  450  for further processing and enhancement. 
     Post-processing circuit  450  is part of color processing stage  312  and performs post-processing of unscaled single color version  446  and first downscaled version  448  to obtain post-processed fused image  472 . Post-processing circuit  450  includes sub-band splitter (SBS) circuit  452 , local tone mapping (LTM) circuit  458 , local contrast enhancement (LCE) circuit  462 , sub-band merger (SBM) circuit  466  and sharpening circuit  470 . SBS circuit  452  performs sub-band splitting of unscaled single color version  446  to generate high frequency component of unscaled single color version  454  passed onto SBM circuit  466 . SBS circuit  452  also performs sub-band splitting of first downscaled version  448  to generate low frequency component of first downscaled version  456  passed onto LTM circuit  458 . LTM circuit  458  performs LTM operation on low frequency component of first downscaled version  456  to generate a processed version of low frequency component of first downscaled version  460  passed onto LCE circuit  462 . LCE circuit  462  performs local photometric contrast enhancement of a single color component (e.g., luma component) of processed version of low frequency component of first downscaled version  460  to generate enhanced version of first downscaled version of fused image  464 . SBM circuit  466  merges high frequency component of unscaled single color version  454  and enhanced version of first downscaled version of fused image  464  to generate merged fused image data  468  passed onto sharpening circuit  470 . Sharpening circuit  470  performs sharpening (e.g., photometric contrast enhancement) on a single color component (e.g., luma component) of merged fused image data  468  to generate post-processed fused image  472 . Post-processed fused image  472  can be passed to output rescale  314  and then output interface  316 . The processing performed at post-processing circuit  450  is merely an example, and various other post-processing may be performed as an alternative or as an addition to the processing at post processing circuit  450 . 
     Example Architecture for Pyramid Generation and Image Fusion 
       FIG. 5  is a block diagram of a pyramid generation and image fusion pipeline, according to one embodiment. The components in  FIG. 5  may belong to a part of the image signal processor  206 , and more specifically, a portion of the image processing pipeline shown in  FIG. 4 . Some of the components in  FIG. 4  are not shown. In various embodiments, the pyramid generation and image fusion pipeline may include different components or additional or fewer components. 
     A clipping marker circuit  424  receives an image  402  (which may be a version of the image  402  such as a processed version  422  shown in  FIG. 4 ) and clip pixel values that are beyond a predetermined range. The predetermined range may restrict color values in a color space (e.g., YCbCr or RGB color space). The predetermined range may represent a range of suitable color values that correspond to a pixel that is neither under-exposed or over-exposed. In one embodiment, the predetermined range may have a lower boundary value and an upper boundary value for a color channel. For example, the lower boundary value may be 0 and the upper boundary value may be 1023 for a color channel. A pixel having a value in a color channel (e.g., luminance, chrominance, R, G, B, etc.) that is lower than the lower boundary value may be considered under-exposed. Likewise, a pixel having a value in a color channel that is higher than the upper boundary value may be considered over-exposed. 
     The clipping marker circuit  424  identifies pixel values that are beyond the predetermined range and replace the pixel values with one or more clipped pixel values. For example, if the image  402  is a long exposure image, the clipping marker circuit  424  may identify a pixel value (e.g., 1057) that is beyond the upper boundary value (e.g., 1023) and replace the pixel value with a clipped pixel value. The clipped pixel value may take a predetermined value such as the upper boundary value or a value that is beyond the boundary as the replaced value. Likewise, the clipping marker circuit  424  may identify under-exposed values that are lower than the lower boundary value and replace those values with clipped pixel values, which may the lower boundary value. 
     The identification of over-exposure and under-exposure may be carried out for different color channels. In one embodiment, the clipping marker circuit  424  may receive the image  402  in YCbCr. For a pixel of the image  402 , the clipping marker circuit  424  may determine whether any of the luminance or chrominance values are beyond the predetermined range. Additionally, or alternatively, the clipper marker may convert the pixel values in image  402  into the RGB format and determine whether any of the color values are beyond the predetermined range in any color channel. 
     The clipping marker circuit  424  may mark the clipped pixel values in various ways. In one embodiment, the over-exposed or under-exposed values are clipped to a predetermined value such as the boundary value, which also serves as markers for subsequent components in the image processing pipeline. Put differently, subsequent components may treat any pixel values at the boundary values as clipped pixel values. In another embodiment, the clipping marker circuit  424  may have a separate identifier that marks any pixel values that have been clipped. 
     The warping circuit  428  performs various linear or non-linear transformations to distort, transform and map an image. The warping circuit  428  may also apply one or more resampling filters to rescale the number of pixels by a fractional ratio. The filters may include different kinds of convolution kernels that transform the image. Each color channel may have a different kind of convolution kernels or different values in the kernels. The resampling process may include filtering, interpolation and decimation that may be performed together in a single step or separately in multiple steps. In performing the filtering, interpolation and decimation, the warping circuit  428  may identify any filtered pixels that are generated from one or more clipped pixel values. The warping circuit  428  may mark the identified filtered pixels with a tag indicating that the identified filtered pixels are generated from the clipped pixel values. 
     A pyramid generator circuit  432  is coupled to the warping circuit  428  or the clipping marker circuit  424  (directly or indirectly) and generates one or more images from the image  402  that may have one or more clipped pixel values. The one or more images generated may be unscaled or downscaled. An unscaled image has the same number of pixels of the image  402 . A downscaled image has a lower number of pixels of the image  402 . In one embodiment, the pyramid generator circuit  432  may generate a downscaled image  550  using the downsampling pipeline  500  and an unscaled image  560 . In some cases, the pyramid generator circuit  432  may repeatedly feed, through a feedback  545 , a first downscaled image to the downscaling pipeline  500  to generate a second downscaled image that is further downscaled from the first downscaled image. In one embodiment, the feedback is performed N−1 times to feed a downscaled image back to the downsampling pipeline  500  to generate a further downscaled image. Accordingly, N images (e.g., an unscaled image  560  and N−1 downscaled images  550 ) are generated by the pyramid generator circuit  432 . With respect to the unscaled image  560 , it may include values of a single channel. For example, the pyramid generator circuit  432  may extract the luminance values of various pixels of the image  402  and generate the unscaled image  560  that has only the luminance values. Put differently, in one embodiment, the unscaled image  560  may be a greyscale version of the image  402 . In other embodiments, the pyramid generator circuit  432  may generate the unscaled image  560  using one or more channels (e.g., Cb, Cr, R, G, or B). The downscaled image  550  may include full channels such as the luminance channel and two chrominance channels. 
     The downscaling pipeline  500  receives an image and downscales the image to a reduced number of pixels through one or more filters and one or more downscaling process. In general, the downscaling pipeline  500  applies one or more image filters to the image  402  to generate a filtered image. A pixel in the filtered image may be generated from a subset of multiple pixels in the image  402 . For example, one of the filters may be a finite impulse response (FIR) filter that generates filtered pixels, which may be weighted averages of different subsets of pixels in the image  402 . Other filters, such as Gaussian, frequency (e.g., high pass, low pass), mean, median, smoothing, blurring, sharpening, un-sharpening, box filters, etc., may also be used. The downscaling pipeline  500  identifies one or more of the filtered pixels that are generated from one or more pixel values that are clipped by the clipping marker circuit  424 . The downscaling pipeline  500  marks the identified filtered pixels in the filtered images with a tag. The tag indicates that the identified filtered pixels in the filtered images are generated from one or more clipped pixel values. The tag may be referred to as an invalid tag. In one embodiment, the downscaling pipeline  500  may also mark other pixels that are not generated with any of the clipped pixel values with a normal tag. The downscaling pipeline  500  also decimates the filtered image to generate the downscaled image  550  that has a reduced number of pixels compared to the image  402 . The downscaled image  550  may include one or more pixels that are marked with the tag to identify for other downstream image processing components the pixel locations that are generated from clipped pixel values. 
     In the downscaling pipeline  500 , the filtering, marking, and decimation processes to generate a downscaled image may be performed by one or more circuits. By way of example, the downscaling pipeline  500  may include a horizontal filter circuit  510 , a horizontal decimation circuit  520 , a vertical filter circuit  530 , and a vertical decimation circuit  540 . In various embodiments, the pyramid generator circuit  432  may include different components or additional or fewer components. For example, in one embodiment, the pyramid generator circuit  432  may perform horizontal and vertical filtering together by using one or more N×M kernels that convolve with the image. N and M may be the same number or a different number. The horizontal and vertical decimations may also be performed in a separable (vertical followed by horizontal, or vice versa) or a non-separable (vertical and horizontal together) fashion. 
     By way of example, the horizontal filter circuit  510  may apply a filter in the horizontal direction to filter a horizontal subset of pixels in a row of the image  402 . The filtering may include different types of computation specified by the filter to mix the values in the subset of pixels. For example, for a filtered pixel in the filtered image, the horizontal filter circuit  510  may include a horizontal FIR filter that generates a filtered value that is a weighted average of a horizontal subset of pixels corresponding to the filtered pixel. In applying the horizontal filter, the horizontal filter circuit  510  may apply padding to the image to preserve the pixel size of the image in the horizontal direction. The horizontal filter circuit  510  may also identify one or more filtered pixels that are generated from clipped pixel values and mark the identified filtered pixels with the tag. The downscaling pipeline  500  may also include a horizontal decimation circuit  520  that decimates the filtered image by a ratio. For example, the decimation ratio may be 2:1 such that the horizontal decimation circuit  520  skips every other pixel in generating a downscaled image. Other ratios are also possible in various embodiments. 
     The vertical filter circuit  530  may be similar to the horizontal filter circuit  510 . The vertical filter circuit  530  may apply a filter in the vertical direction to filter a vertical subset of pixels in a column of the image  402 . The filtering may also include different types of computation specified by the filter. For example, for a filtered pixel in the filtered image, the vertical filter circuit  530  may include a vertical FIR filter that generates a filtered value that is a weighted average of a vertical subset of pixels corresponding to the filtered pixel. In applying the vertical filter, the vertical filter circuit  510  may apply padding to preserve the pixel size of the images in the vertical direction. The vertical filter circuit  510  may also identify one or more filtered pixels that are generated from clipped pixel values and mark the identified filtered pixels with the tag. The downscaling pipeline  500  may also include a vertical decimation circuit  540  that decimates the filtered image by a ratio. For example, the decimation ratio may also be 2:1, which may be the same as the horizontal decimation. Other ratios are also possible in various embodiments. 
     The particular arrangement of the downscaling pipeline  500  shown in  FIG. 5  is for example only. In one embodiment, the vertical filter circuit  530  and the vertical decimation circuit  540  may instead be located upstream of the horizontal filter circuit  510  and the horizontal decimation circuit  520 . In another embodiment, the horizontal and vertical filtering and decimation may be combined and performed together. In various embodiments, the decimation ratio may be the same or different for horizontal and vertical decimation. The horizontal and vertical filters may also be the same or different. In one embodiment, the horizontal filter circuit  510  may include more than one filters, such as a first filter specific to filtering a first channel (e.g., luminance values) and a second filter specific to filtering a second channel (e.g., chrominance values). The first and second filter may be of the same support size but may have different values such as different weights. For example, both filters may be FIR filters but specify different weights in the filters. Likewise, the vertical filter circuit  530  may also include different filters for luminance values and chrominance values. 
     The terms horizontal and vertical (or rows and columns) may describe two relative spatial relationships. While, for the purpose of simplicity, a column described herein is normally associated with a vertical line of an image, it should be understood that a column does not have to be arranged vertically (or longitudinally). Likewise, a row does not have to be arranged horizontally (or laterally). Vertical and horizontal directions in this disclosure may also be referred to as a first direction and a second direction that is different from the first direction. The two directions also do not necessarily mandate any specific spatial relationship such as any parallel or perpendicular arrangement. 
     In some cases, the image signal processor  206  also receives a second image  404 . The second image  404  may capture the same scene as the first image  402  but have a different exposure time. For example, the first image  402  may be a long exposure image and the second image  40  may be a short exposure image. A pyramid generator circuit  440  may process the second image  404  to generate a plurality of images, which may include a plurality of downscaled images  570  and an unscaled image  580 . The pyramid generator circuit  440  may have the same or different structure and arrangement as the pyramid generator circuit  432 . For example, in one embodiment, the pyramid generator circuit  440  may convert the second image  404  to the downscaled image  570  that includes full color channels using filters and downscaling ratios that are similar to the pyramid generator circuit  432  but the pyramid generator circuit  440  does not mark any pixels with a tag. In another embodiment, since the second image  404  may be a short exposure image, the second image  404  may be clipped for pixels that are under-exposed. The pyramid generator circuit  440  may mark the filtered pixels in the filtered image that are generated from the clipped pixels similar to the process of the pyramid generator circuit  432 . With respect to the unscaled image  580 , it may be similar to the unscaled image  560 , which may only have a single color channel. For example, the pyramid generator circuit  440  may extract the luminance values of the second image so that the unscaled image  580  may be a greyscale version of the second image  404 . 
     The image fusion processor  444  is coupled to the pyramid generator circuit  440  (directly or indirectly) and receives the images outputted from the pyramid generator circuits  432  and  440  to generate one or more fused images  446  and  448  by fusing images of the same resolutions. For example, the image fusion processor  444  fuses the unscaled image  560  and the unscaled image  580  to generate an unscaled fused image  446 . The image fusion processor  444  also fuses different pairs of a downscaled image  550  and a downscaled image  570  that have the same resolution to generate a plurality of downscaled fused images  448 . The image fusion processor  444  may receive N images from each pyramid generator circuit. 
     The fusing may be a per-pixel blending between two images. For example, in fusing a pair of downscaled images  550  and  570 , for a pixel of the fused image  448 , the image fusion processor  444  may blend the pixel values that correspond to the same pixel location in the downscaled images  550  and  570 . The blending may include taking a weighted average to generate a fused pixel of the fused image  448 . In performing the fusion, the image fusion processor  444  identifies filtered pixels in the downscaled image that are marked with an invalid tag that indicates the filtered pixels are generated from one or more clipped pixel values. The image fusion processor  444  disregards those filtered pixels that are marked with the invalid tag. For example, for a first fused pixel of the downscaled fused image  448  that is to be generated from a first filtered pixel that is not marked with the invalid tag in the downscaled image  550 , the image fusion processor  444  determines a first weighted average between the first filtered pixel in the downscaled image  550  and a first corresponding pixel in the downscaled image  570 . In contrast, for a second fused pixel that is to be generated from a second filtered pixel that is marked with the invalid tag in the downscaled image  550 , the image fusion processor  444  disregards the second filtered pixel in the downscaled image  550  and uses a second corresponding pixel in the downscale image  570  that is not marked with an invalid tag as the value of the second fused pixel. 
     The first and second fused images  446  and  448  may be combined to generate a high dynamic range image that has a dynamic range higher than the first image  402  and the second image  404 . 
     Example Filtering and Decimation 
       FIG. 6  is a series of conceptual diagrams illustrating a filtering and decimation process that may be performed by the image signal processor&#39;s one of the image processing circuit, such as the pyramid generator circuit  432 , according to an embodiment. The warping circuit  428  may also perform a similar image processing operation. For illustration, the figure is described using the pyramid generator circuit  432 , but in different embodiments other image processing circuits may also perform a similar operation. Pixel blocks  610 ,  620 , and  630  show the same image, such as the image  402  shown in  FIG. 5 , with a filter  670  that is applied at different pixels. Pixel block  640  shows a first filtered image. Pixel block  650  shows a second filtered and decimated image. Pixel block  655  shows an image that is both vertically and horizontally filtered. Pixel block  660  shows a final downscaled image, such as the downscaled image  550  shown in  FIG. 5 . While  FIG. 6  shows 32 pixels in the image  402 , an actual image  402  may be in a higher resolution and may include millions of pixels. A single pixel may be associated with a set of values, which represent values of different color channels. For simplicity, only example values of one of the color channels for some of the pixels of the images are shown. Also, not all pixel values are shown. 
     The pyramid generator circuit  432  may apply a horizontal filter  670  to the image. Pixel blocks  610 ,  620 , and  630  show the horizontal filter  670  applied at different locations. The support size of the horizontal filter  670  may be selectable by the pyramid generator circuit  432 , such as by an external command. For example, the support size of the horizontal filter  670  is 1×3. In other words, the horizontal filter  670  filters pixel values of the middle pixel with the pixel values of two adjacent pixels, one left and one right of the middle pixel. In another case, the pyramid generator circuit  432  may select a 1×5 filter that filters pixel values of the middle pixel with the pixel values of four adjacent pixels, two left and two right of the middle pixel. In yet other cases, the pyramid generator circuit  432  may select a 1×7 filter or a 1×9 filter. The pixels that are selected for filtering may also not be symmetric about the middle pixel. In another embodiment, the pyramid generator circuit  432  may perform filtering and decimation in both horizontal and vertical direction together. In such an embodiment, the filter may a 3×3 kernel, 5×5 kernel, 7×7 kernel, etc. and the support window size of the kernel may be selectable by the pyramid generator circuit  432 . 
     The pyramid generator circuit  432  may use an FIR filter to filter the pixel values and applies a morphological erosion mask to mark filtered pixels with an invalid tag. Using morphological erosion, the pyramid generator circuit  432  marks a filtered pixel with the invalid tag if one or more of the pixel values in the subset of pixels that are used to generate the filtered pixel is a clipped pixel value. The pyramid generator circuit  432  may identify the clipped pixel value by tags in the input image or alternatively find the pixels whose values are equal to a predetermined value that is at the boundary or outside the valid pixel range (e.g., 1023 or value that is larger than 1023). For example, the pyramid generator circuit  432  may treat a pixel value having the boundary value of a predetermined range as a clipped pixel value. For example, if the predetermined range is between 0 and 1023, the pyramid generator circuit  432  may identify the pixel B 4  in the input image as a clipped pixel value. 
     In pixel block  610 , the filter  670  is applied at the pixel B 2 . The filter  670  may be an FIR filter that filters the pixel values of a subset of pixels. When the filter  670  is applied at the pixel B 2 , the subset of pixels includes the pixel B 2  and the two pixels that are adjacent to the pixel B 2  (e.g., pixels B 1  and B 3 ). The weighted average may be a simple average that weights the three pixel values equally or a weighted average that weights the middle pixel more heavily than the adjacent pixels. For the purpose of illustration, a simple average is used in the example shown in  FIG. 6 . As a result, the filtered pixel value for pixel B′ 2  of the filtered image shown in pixel block  640  may take the value of 333, which is the average of the pixel values B 1 , B 2 , and B 3  in the subset of pixels. 
     In pixel block  620 , the filter  670  is shifted to pixel B 3 . The subset of pixels that are used to generate the filtered pixel B′ 3  in the filtered image in pixel block  640  becomes pixels B 2 , B 3 , and B 4 . As such, the filtered pixel value for pixel B′ 3  may be an average of the pixels B 2 , B 3 , and B 4 . The pyramid generator circuit  432  identifies that the pixel B 4  has the boundary value 1023. Hence, the pyramid generator circuit  432  treats the pixel B 4  as a clipped pixel value. Since the filtered pixel B′ 3  of the filtered image in the pixel block  640  is generated from one or more clipped pixel values, the pyramid generator circuit  432  marks the filtered pixel with an invalid tag indicating that the filtered pixel B′ 3  is generated from one or more of the clipped pixel values. The invalid tag may take different forms in various embodiments. For example, in one embodiment, the invalid tag may be a tag value (e.g., 0) that is different from the tag value (e.g., 1) of pixels that are not generated from any clipped pixel values. The tag values may be separated from the pixel values and may be stored in different registers of the pyramid generator circuit  432 . In another embodiment, a pixel value may be used directly as a tag. For example, for a filtered pixel that is generated from one of the clipped pixel value, the pyramid generator circuit  432  may force the filtered pixel value to a boundary value (e.g., 1023) regardless what the average value is. As such, downstream components and pipelines, such as the image fusion processor  444 , may identify those filtered pixels by the boundary value. In yet another embodiment, the pyramid generator circuit  432  may both use a tag that is separated from the pixel value and force the pixel value to the boundary value. For illustration, the filtered pixels that are generated from one of the clipped pixel values are marked with a cross X in  FIG. 6 . 
     In pixel block  630 , the filter  670  is shifted to pixel B 4 . Since pixel B 4  is a clipped pixel value, the filtered pixel B′ 4  in the filtered image shown in pixel block  640  is marked with an invalid tag. Likewise, when the filter  670  is shifted to pixel B 5  (not shown in  FIG. 6 ), the filtered pixel B′ 5  is still generated from the clipped pixel value in the pixel B 4 . As such, the filtered pixel B′ 5  is also marked with an invalid tag. For the filtered pixel B′ 6 , the subset of pixels in the input image that generates the filtered pixel B′ 6  includes pixels B 5 , B 6 , and B 7 . None of the pixels in this subset is a clipped pixel value. As such, the pyramid generator circuit  432  generates a weighted average value in filtered pixel B′ 6 . In pixel block  640 , assume that no pixel in the rows A′ and C′ is generated from a clipped pixel value. The filtered pixel values at A′ 3 , A′ 4 , A′ 5 , C′ 3 , C′ 4 , and C′ 5  are also shown. 
     The pyramid generator circuit  432  may apply a horizontal decimation, as indicated by the arrow  680 . The horizontal decimation may reduce the number of columns by half. Hence, columns 2, 4, 6, 8 in the filtered image shown in pixel block  640  are removed. 
     The pyramid generator circuit  432  may also apply a vertical filter, as indicated by the arrow  685 . In applying vertical filtering, since the filtered pixels B′ 3  and B′ 5  are marked with an invalid tag, the pyramid generator circuit  432  performs a morphological erosion to spread the invalid tags to vertically adjacent pixels. As such, filtered pixels A′ 3 , A′ 5 , C′ 3 , and C′ 5  are also marked with the invalid tag in pixel block  655 . As a result of using an erosion mask as one of the filters, the number of filtered pixels that are marked with the invalid tag in a filtered image becomes larger than the number of pixels in the input image (e.g., image  402 ) that are associated with clipped pixel values. The pyramid generator circuit  432  may select the extent of erosion by applying filters of different sizes. For example, the invalid tag may be spread to more pixels when a filter of support size  5  is applied instead of the filter  670  of support size  3 . 
     The pyramid generator circuit  432  may further vertically decimate the filtered image shown in pixel block  650  to generate a downscaled image shown in pixel block  660 . When the input image includes one or more clipped pixel values, the downscaled image may include one or more pixels that are marked with the invalid tag. 
     Example Resampline Process 
       FIG. 7  is a series of conceptual diagrams illustrating a resampling process that may be performed by the image signal processor&#39;s one of the image processing circuits, such as the warping circuit  428 , according to an embodiment. The pyramid generator circuit  432  may also perform a similar image processing operation. For illustration, the figure is described using the warping circuit  428 , but in different embodiments other image processing circuits may also perform a similar operation. 
     A resampling process may include one or more interpolation processes and one or more decimation processes to change the number of pixels of an image by a resampling ratio. The resampling ratio may be a fractional value such as ⅔. In one embodiment, the resampling ratio may be 1536/2304, 1152/1536, or other suitable ratios for the image signal processor. In addition to interpolation and decimation, the warping circuit  428  may carry out a filtering process that applies one or more filters such as different kinds of convolution kernels to perform image distortion, transformation, and mapping. The filtering may be similar to the filtering process illustrated in  FIG. 6 . The precise filtering process may depend on the coefficients and support windows of the filters. The filtering process is not repeated in  FIG. 7 . 
     Pixel block  710  illustrates an input image of a warping circuit  428 , such as the processed image  426  shown in  FIG. 4 . Pixel block  720  shows an interpolated image and pixel block  730  shows a decimated image. Similar to  FIG. 6 , while the pixel block  710  in  FIG. 7  shows only 24 pixels, an actual image may be in a higher resolution and may include millions of pixels. A single pixel may be associated with a set of values, which represent values of different color channels. For simplicity, only example values of one of the color channels for some of the pixels in row B of the images are shown. 
     In performing a resampling process, the warping circuit  428  may interpolate the pixel values of the image by a first ratio and decimate the interpolated image by a second ratio that is different from the first ratio. The types of interpolation used may depend on the embodiments. In one embodiment, the interpolation may be separable into horizontal interpolation and vertical interpolation. The window size of the interpolation and filter may be in any suitable size such as 1×2, 1×3, and 1×4. In another embodiment, the vertical and horizontal interpolation is non-separable. Put differently, the vertical and horizontal interpolations are carried out together. A 4×4 bicubic interpolation, a 2×2 bilinear interpolation, or any other suitable interpolations may be used. 
     In  FIG. 7 , a horizontal interpolation that increases the number of pixels by a ratio of 2 is illustrated. Pixel block  720  shows an interpolated image that includes a plurality of filtered pixels. In generating an interpolated value, the warping circuit  428  determines the value based on original pixel values. For example, in one embodiment, the filtered pixel value B′ 2  in the interpolated image in pixel block  720  is 250, which is interpolated from the pixels B 1  and B 2  in the pixel block  710 . Likewise, the filtered pixel value B′ 4  and B′ 10  respectively have the interpolated values of 350 and 350. In performing the interpolation, the warping circuit  428  may mark a filtered pixel with an invalid tag if the interpolation is derived from one or more pixels having values that are marked as clipped. The warping circuit  428  may identify the clipped pixel value by tags in the input image or alternatively find the input pixels whose values are equal to a predetermined value that is at the boundary or outside the valid pixel range (e.g., 1023 or value that is larger than 1023). For example, pixel B 4  in the pixel block  710  is identified as a clipped value. As a result, the filtered pixels B′ 6  and B′ 8 , which are generated from interpolation of pixels that include the pixel B 4  in the pixel block  710 , are marked with a cross X in  FIG. 7 , which represent an invalid tag. In another embodiment, the marking may also be done by forcing the filtered pixels B′ 6  and B′ 8  that a predetermined value such as 1023 to signify the filtered pixels are invalid. For example, the interpolated value for the filtered pixels B′ 6  and B′ 8  should be about 712. However, because pixel B 4  is a clipped value, the filtered pixels B′ 6  and B′ 8  are either marked with a cross X or being forced to take the value 1023. The filtered pixel B′ 7  takes the value of the input pixel B 4  in pixel block  710 . Hence, it is also marked with a cross X. 
     The warping circuit  428  also decimate the filtered image shown in pixel block  720  to generate a decimated image shown in pixel block  730 . The decimation ratio may be different from the interpolation ratio. In the example shown in  FIG. 7 , a decimation ratio of 3 is used for the horizontal direction. As a result, every three pixels may be extracted to the decimated image. Hence, the decimated image shows pixel values in columns 1, 4, 7, and 10. Any pixel marked with the invalid tag is continued to be marked so. Before or after the horizontal interpolation and horizontal decimation, a vertical interpolation and decimation may be carried out. In some embodiments, horizontal and vertical image processing may be carried in a single step. Filtering, interpolation, and decimation may also be carried in a single step by applying convolution kernel, padding, striding, and other suitable image processing technique. Output pixels that are generated from clipped pixel values are marked as invalid. 
     Example Pyramid Generation and Image Fusion Process 
       FIG. 8  is a flowchart depicting an example pyramid generation and image fusion process that may be performed by an image signal processor, according to an embodiment. A portable electronic device may include an image sensor system that captures a plurality of images of a same scene. The plurality of images may include a first image that corresponds to a first exposure time and a second image that corresponds to a second exposure time that is shorter than the first exposure time. The portable electronic device may also include an image signal processor that fuses the two images to generate a high dynamic range image for display in the touch screen of the portable electronic device. 
     By way of example, the image signal processor receives the first image and the second image from the image sensor system. The image signal processor identifies  810  one or more pixel values in an image that are beyond a predetermined range. The image may be the first image, which is the long exposure image. The image signal processor replaces  820  the one or more identified pixel values with one or more clipped pixel values. For example, the image signal processor may set the pixel values that are beyond the predetermined range to a boundary value. 
     The image signal processor may include a pyramid generator circuit that generates one or more downscaled images of the image that is inputted to the pyramid generator circuit (e.g., the first image). In one embodiment, the pyramid generator circuit applies  830  one or more image filters to the image to generate a filtered image that includes filtered pixels. A filtered pixel may be generated from a subset of pixels in the input image. The filters may specify the computations used for the filtering, which may include averaging, blurring, sharpening, etc. The application of the filters may include padding to make the support size of the filtered image in the application direction of the filter (e.g., horizontal or vertical) the same as the input image. 
     The image signal processor identifies  840  one or more of the filtered pixels that are generated from the one or more clipped pixel values in the input image. For example, in one embodiment, the image signal processor may generate one of the filtered pixels from a subset of pixel values of the input image. The image signal processor may determine that one of the pixel values in the subset has a boundary value of the predetermined range. In response to one of the pixel values in the subset of the input image has the boundary value, the image signal processor may treat such pixel as having a clipped pixel value. In another embodiment, the image signal processor may identify the clipped pixel values by other methods. For example, the input image may have tags that are labeled by a clipping marker circuit that is upstream of the pyramid generator circuit. 
     The image signal processor marks  850  the identified filtered pixels with a tag indicating that the identified filtered pixels are generated from the one or more clipped pixel values. In one embodiment, the pixels in the input image may be assigned with pre-assigned with a normal tag. When one of the filtered pixels that are generated from a clipped pixel value is identified, the normal tag of the identified filtered pixel is replaced with an invalid tag. Additionally, or alternatively, the image signal processor may also set the pixel value in the identified filtered pixel to the boundary value of the predetermined range. The boundary value may serve as a tag indicating that the filtered pixel is generated from a clipped pixel value. The image signal processor decimates  860  the filtered image to generate a downscaled image having a reduced number of pixel compare to the image. The downscaled image may include one or more pixels marked with the invalid tag. 
     The image signal processor may also receive a second image captured by the image sensor system. The image signal processor downscales the second image to generate a second downscaled image. The image signal processor fuses  870  the first downscaled image generated from the first image with a version of the second image, such as the downscaled second image. In the fusion, the resolution of the two downscaled images may be the same. The image signal processor may perform a per-pixel blending to blend a first pixel from the first downscaled image with a corresponding second pixel from the second downscaled image the filtered pixels to generate a fused pixel. In the fusion, the filtered pixels in the first downscaled image that are marked with the invalid tag are disregarded. For example, when the first pixel from the first downscaled image is marked with the invalid tag, the image signal processor disregards the first pixel and takes a second pixel from the second downscaled image at the corresponding location of the first pixel as the value of the fused pixel in the fused 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: 20190422
Publication Date: 20210601
Grant Date: 20210601
Priority Date: 20190422
Inventors: SMIRNOV, MAXIM
LAMBURN, ELENA
KEREM, OREN
WU, CHIHSIN
Assignee: APPLE INC
CPC Classifications: [{"code": "G06T5/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2200/28", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T3/4007", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/30", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/20221", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/50", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T3/4023", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T2207/20016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20208", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20221", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20221", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/20016", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T3/4023", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/009", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/20208", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T5/30", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/4007", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/0093", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/92", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/18", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 72832605