Patent Publication Number: US-11023994-B2

Title: Auto-focus engine architecture for image signal processor

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates to a circuit for processing images and more specifically to an auto-focus circuit of an image signal processor. 
     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. 
     An electronic device may include multiple image sensors that serve different functions such as capturing images and videos and performing facial recognition. The image sensors are often associated with lenses and suitable optical components for capturing images of objects at different distances. Auto-focusing relates to adjusting the lens position of an image sensor using a processor. The processing of focus related data could occupy significant resources of the electronic device. Also, the speed of an image sensor to adjust its focus may affect user experience in using an electronic device. 
     SUMMARY 
     Embodiments relate to an image signal processor that includes circuitry for auto-focusing of an image sensor. An auto-focus circuit may be operably coupled to the image sensor to receive raw image data captured by the image sensor. The auto-focus circuit may determine a phase shift among focus pixel values in the raw image data. The focus pixel values represent pixel values from the focus pixels of the image sensor. Based on the phase shift, the auto-focus circuit may generate a signal that is sent to the image sensor to change the lens position of the image sensor. The image signal processor may include a statistics circuit separate from the auto-focus circuit. The statistics circuit may obtain statistical information on the raw image data by processing the raw image data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level diagram of an electronic device, according to one embodiment. 
         FIG. 2  is a block diagram illustrating components in the electronic device, according to one embodiment. 
         FIG. 3  is a block diagram illustrating image processing pipelines implemented using an image signal processor, according to one embodiment. 
         FIG. 4  is a conceptual diagram illustrating an example image sensor, according to one embodiment. 
         FIG. 5  is a block diagram illustrating an example auto-focus pipeline of an image signal processor, according to one embodiment. 
         FIG. 6  is a flowchart illustrating a method of operating an image signal processor having an auto-focus circuit, 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 of the present disclosure relate to image signal processors (ISP) that include one or more auto-focus circuits that are separate from a statistics circuit and other image processing pipelines of the ISP. An image sensor may include one or more focus pixels that are used to generate data for auto-focusing. The auto-focus circuit may extract the focus pixel values to generate a signal to control the lens position of the image sensor based on the phase shift of the focus pixels. The auto-focus circuits may also include circuits that generate exposure statistics of the image sensors. Each image sensor may be connected to a separate auto-focus circuit. The separation of the auto-focus circuits from other image processing pipelines may allow continuous processing of focus data without impacting the performance of other pipelines. 
     Example 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. 
       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. Image data in a Bayer pattern or other patterns that have a monochromatic color value for each pixel may be referred to as “raw image data” herein. 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  processes 2 pixels per clock cycle, then raw processing stage  306  operations (e.g., black level compensation, highlight recovery and defective pixel correction) may process 2 pixels of image data at a time. In contrast, one or more back-end pipeline stages  340  may process image data at a different rate less than the initial data rate. For example, in the embodiment of  FIG. 3 , back-end pipeline stages  340  (e.g., noise processing stage  310 , color processing stage  312 , and output rescale  314 ) may be processed at a reduced rate (e.g., 1 pixel per clock cycle). 
     Raw image data captured by image sensors  202  may be transmitted to different components of ISP  206  in different manners. In one embodiment, raw image data corresponding to the focus pixels may be sent to the auto-focus circuits  350  while raw image data corresponding to the image pixels may be sent to the sensor interface  302 . In another embodiment, raw image data corresponding to both types of pixels may simultaneously be sent to both the auto-focus circuits  350  and the sensor interface  302 . 
     Auto-focus circuits  350  may include hardware circuits that analyze 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 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 the 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 3A 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 input 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 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 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 Image Sensors 
       FIG. 4  is a conceptual diagram illustrating an example image sensor  202 , according to one embodiment. The image sensor  202  includes a plurality of image sensing units  410  (also referred to as “active pixel sensors,” “image sensing pixels,” or simply “pixels”), as described above with reference to  FIG. 2 . Each image sensing unit  410  may detect light of a particular wavelength or range of wavelength. In  FIG. 4 , each individual image sensing unit  410  is represented by a square with a letter that represents the color that the image sensing unit is designed to detect. For example, an image sensing unit  410  with the letter “G” represents an image sensing unit that detects green light. Letter “R” represents an image sensing unit that detects red light, and “B” represents an image sensing unit that detects blue light. While the image sensor  202  shown in  FIG. 4  includes image sensing units  410  that are designed to detected red, green and blue light, in other embodiments an image sensor  202  may also include other types of image sensing units  410  that detect visible light or other electromagnetic waves of different wavelengths. 
     The image sensing units  410  may be arranged in a specific pattern. In the example of  FIG. 4 , a pattern of four pixels  420  in a square format (referred to as a “pixel quad”) are repeated in both horizontal and vertical directions. The specific arrangement of  FIG. 4  is referred to as a Bayer format and the pixel quads having this arrangement may be referred to as Bayer quads. In various embodiments, other variations of Bayer formats or changes in the positions of image sensing units  410  in different colors may also be used. For example, the top left and bottom right corners of a quad may include green pixels. 
     The image sensing units  410 , which can also be referred to as pixels of the image sensor  202 , may include different types of pixels. For example, the image sensing units  410  may include image pixels  430  and focus pixels  440 . Image pixels  430  are image sensing units  410  that generate image data values for the electronic device  100  to form an image. Focus pixels  440  generate image data values for auto-focus circuits  350  in the electronic device  100  to determine a focus position of an image sensor  202 . In some embodiments, the focus pixels  440  or the image pixels  430  may serve dual purposes for both forming an image and providing auto-focus data. In other embodiments, the focus pixels  440  and the image pixels  430  may have their own respective specialized roles. In the embodiment shown in  FIG. 4 , the focus pixels  440  are shown as square boxes that have half of the pixels covered (e.g., pixels  412  and  414 ). The image pixels  430  are shown as square boxes without any cover. In various embodiments, the focus pixels  440  may be distributed in various locations and patterns. A portion of each focus pixel  440  may be blocked from light exposure, which is illustrated in  FIG. 4  as the focus pixels  440  being partially covered. The portion of the focus pixel  440  being blocked may be located in the left, right, top, bottom, diagonal, or any other areas, symmetric or asymmetric, regular or irregular, and equally divided or not, of the focus pixel. Different focus pixels may be blocked in different portions. For example, a first focus pixel  412  may have a first portion (shown as, e.g., the left side) blocked from light exposure while a second focus pixel  414  may have a second portion (shown as, e.g., the right side) block from light exposure. The second portion blocked in the second focus pixel  414  may be different from a corresponding portion blocked in the first focus pixel  412 . The blocking of different corresponding portions of the focus pixels allows the focus pixel to capture images from slightly different angles compared to nearby focus pixels. In turn, an auto-focus circuit  350  may determine auto-focus data based on pixel values transmitted from the focus pixels in a manner that will be discussed in further detail in  FIG. 5 . 
     By way of example, the electronic device  100  may have a dual camera system that has a first image sensor  202 A equipped with a wide-angle lens and a second image sensor  202 B equipped with a telephoto lens. The two image sensors  202 A,  202 B with different focal lengths cooperate to capture images and videos. Depending on the distances of the objects captured by the image sensors  202 , the ISP  206  may process the raw image data from one of the image sensors  202  and to display the processed images on the touch screen  150 . The image sensor  202  that is transmitting raw image data to other pipelines of ISP  206  is an active image sensor while another image sensor  202  is in standby. The auto-focus circuit  350  connected to the standby image sensor  202  continues to generate focus signals  592  to keep the standby image sensor  202  remain in focus. When the electronic device  100  switches the images displayed on the touch screen  150  to the processed images captured by the standby image sensor  202 , the standby image sensor  202  may immediately generate raw image data in focus to other image processing pipelines of the ISP  206  without delay. 
     Example Auto-Focus Circuit Pipelines 
       FIG. 5  is a block diagram illustrating auto-focus circuit pipelines of an image signal processor (ISP)  206 , according to one embodiment. For simplicity, various components of the ISP  206 , such as the sensor interface  302 , front-end  330 , back-end  340 , and output interface  316  (which are shown in  FIG. 3 ), are not shown in  FIG. 5  except an arrow indicating image data may be sent to other image processing pipelines. The ISP  206  includes one or more auto-focus circuits  350 A through  350 N (collectively referred to as “auto-focus circuits  350 ” or referred individually as “auto-focus circuits  350 ”). Each of auto-focus circuits  350 A through  350 N may be operably coupled to an image sensor  202  that transmits raw image data captured by the image sensor. In one embodiment, the number of auto-focus circuit  350  may correspond to the number of image sensors  202  in the electronic device  100  so that each image sensor  202  has its own auto-focus circuit  350 . In another embodiment, the number of auto-focus circuit  350  may correspond to the number of a particular type of image sensors  202 , for example, the number of cameras in the electronic device  100 . In other embodiments, multiple image sensors  202  may share one auto-focus circuits  350 . 
     The ISP  206  may include a statistics circuit such as the image statistics circuit  304  that is separate from each of the auto-focus circuits  350 . The statistics circuit  304  may obtain statistical information on the image data generated by the image sensors  202 , as described above with reference to  FIG. 3 . 
     The auto-focus circuits  350  are circuits that generate focus signals  592  based on image data captured by the image sensors  202  to change the lens position of the image sensors  202 . The auto-focus circuits  350  are separate and distinct from the image statistics circuit  304 . An auto-focus circuit  350  may be operably coupled to an image sensor  202  to receive raw image data captured by the image sensor  202 . The auto-focus circuit  350  may also be coupled to a control circuit  590  of the image sensor system  201 . The control circuit  590  receives the focus signals  592  and controls focusing mechanisms (e.g., a mechanical actuator) in the image sensors  202 . The image sensor system  201  may include a single control circuit  590  or multiple control circuits  590  for controlling different types of image sensors  202 . For a pair of auto-focus circuit  350  and image sensor  202 , the auto-focus circuit  350  may determine focus data based on the image data generated by the image sensor  202 . 
     An auto-focus circuit  350  may generate a focus signal  592  by, for example, using a phase detection technique that determines a phase shift among focus pixel values in the image data of the image sensor  202 . The focus signal  592  generated by the auto-focus circuit  350  may be a direct control signal that is sent to the control circuit  590  or a signal that includes focus data for the control circuit  590  to generate a control signal. 
     By separating the auto-focus circuits  350  from the front-end pipeline stages  330 , the back-end pipeline stages  340 , and the image statistics circuit  304 , the ISP  206  may advantageously process image data from one of the image sensors  202  while each of the auto-focus circuits  350  continues to process and generate focus signals  592  to send to the image sensor system  201  to control the lens position of its respective image sensor  202 . While ISP  206  shown in  FIG. 5  has a separate auto-focus circuit  350  for each image sensor  202 , in some embodiments one or more image sensors  202  may also share an auto-focus circuit  350  that is separate from the image statistics circuit  304  and from other image processing circuits of ISP  206 . When multiple auto-focus circuits  350  are provided to control each image sensor  202 , each of the auto-focus circuits  350  may continue to maintain a corresponding image sensor in focus regardless of which image sensor  202  is providing the raw image data for further processing by other pipelines of the ISP  206 . Hence, whenever a switch is made from one image sensor  202  to another image sensor  202 , the switched image sensor remains in focus and provides in-focus raw image data without delay. 
     In one embodiment, an auto-focus circuit  350  includes, among other components, a crop (CROP) circuit  510 , a patterned defect pixels (PDP) processing circuit  520 , a gain offset clamp (GOC) circuit  530 , an auto-focus engine (AFE) circuit  540 , a subsample binner (SBIN) circuit  550 , a radial vignetting correction (RVC) circuit  560 , a histogram (HIST) generation circuit  570 , and a min max sum (MIMS) circuit  580 . The auto-focus circuits  350  in various embodiments may include different, fewer or additional circuit units. Also, depending on embodiments, fewer or more than two auto-focus circuits  350 , each with the same or different components, may be provided. 
     The crop (CROP) circuit  510  is a circuit that reduces the size of the raw image data by cropping the raw image data. To reduce consumed resources (e.g., power and memory) associated with processing the raw image data, the CROP circuit  510  crops the raw image data to a reduced size. The CROP circuit  510  may operate in different modes. For example, one of the modes may enable selective processing of parts of the raw image data. In another operation mode, the cropping operation may start at a location and continue in a horizontal direction or in a vertical direction. In yet another operation mode, the cropping operation may crop the image based on a frame width or frame height of a plurality number of pixels. In some embodiments, the crop circuit may be bypassed. 
     The patterned defect pixels (PDP) processing circuit  520  is a circuit that identifies and processes focus pixels in the raw image data. The PDP processing circuit  520  receives a cropped or original raw image data with known focus pixel locations in the received image data. The locations of focus pixels may be predetermined by the placement of focus pixels in the image sensor pixel array. Focus pixels  440  may have lower color values compared to ordinary pixels since focus pixels are partially covered. After the PDP processing circuit  520  identifies focus pixels, the color values of these pixels may be increased relative to color values of ordinary pixels. 
     The gain offset clamp (GOC) circuit  530  is a circuit that performs a black level compensation for each color component in the raw image data. An image sensor  202  may include image sensing units that have an inherent noise level that results in a non-zero (positive or negative) reading even if the image sensor  202  is completely blocked from light exposure. An offset operation may be performed at the image sensor  202  based on the black level noise so that the output of the image sensor becomes positive. For this purpose, the GOC circuit  530  may perform linear transformation operations and clipping to an input image to remove the offset effect performed at the image sensor  202  and also keep the saturation level of the image at the same level. 
     The auto-focus engine (AFE) circuit  540  is a circuit that generates the focus signal  592  for the image sensor  202  to cause the corresponding image sensor  202  to change its lens position. The AFE circuit  540  may receive a version of the raw image data processed by the PDP processing circuit  520  and GOC circuit  530 . The AFE circuit  540  may perform pixel corrections for the focus pixel values to calculate what the pixel values would be if the portions of focus pixels were not blocked from light exposure. The pixel correction for a focus pixel value may be performed by processing the focus pixel value to neighboring image pixel values. After the focus pixel values are corrected, the AFE circuit  540  may determine a phase shift among focus pixel values in the image data. Because different portions of different focus pixels are blocked from light exposure in the image sensor  202 , the angle of each of the different focus pixels in capturing an image is slightly different. Thus, a disparity in the form of phase shift exists among the focus pixel values. For example, in one embodiment, the AFE circuit  540  may select, from the plurality of focus pixel values extracted in the PDP processing circuit  520 , a first focus pixel value that corresponds to a first focus pixel with a first portion blocked from light exposure. The AFE circuit  540  may calculate a first corrected focus pixel value that estimates the first focus pixel value if the first portion were not blocked. Likewise, the AFE circuit  540  may select, from the plurality of focus pixel values, a second focus pixel value that corresponds to a second focus pixel with a second portion blocked from light exposure. The second portion may be different from a corresponding portion blocked in the first focus pixel. For example, the first focus pixel may be blocked from the left side and the second focus pixel may be blocked from the right side. The AFE circuit  540  may calculate a second focus pixel value that estimates the second focus pixel value if the second portion were not blocked. The AFE circuit  540  may determine a phase shift from a difference between the first and the second focus pixel values. In another embodiment, the AFE circuit  540  may determine a phase shift from a difference between a focus pixel value and its corrected pixel value. Based on the phase shift, the AFE circuit  540  may generate the focus signal  592  that causes the image sensor  202  to change the lens position. 
     In some embodiments, the pipeline of an auto-focus circuit  350  is completed after the AFE circuit  540 . In other embodiments, other auto-focus circuits  350  may include more downstream circuits to perform other operations and signal processing on the image data for controlling other aspects of image sensors. For example, the auto-focus circuits  350 , which are separate from the image statistics circuit  304 , may include their own statistics units that generate various statistics that are relevant to the control of the image sensors, such as statistics related to exposures of the image sensors. Hence, the independent auto-focus circuit pipelines that are separate from the main image statistics circuit  304  and other image processing pipelines allow the ISP  206  to control other aspects of the image sensors  202  while the image sensors  202  are in standby. For example, the exposure statistics allows image sensors  202  to have the right levels of ISO, shutter speed or aperture size on top of the right lens position even though the image sensors  202  are in standby. Hence, the images displayed at the touch screen  150  may transition seamlessly without a sudden change in contrast or brightness level when the active image sensor  202  changes from one sensor to another. 
     In one embodiment, an auto-focus circuit  350  includes an SBIN circuit  550 , an RVC circuit  560 , a HIST generation circuit  570 , and an MMS circuit  580  that are designed for generating exposure statistics from the image data (e.g., a version of the raw image data) generated by an image sensor  202 . 
     The subsample binner (SBIN) circuit  550  down-samples the raw image data to reduce the pixel rate of the images. The SBIN circuit  550  converts a version of the raw image data to an output image that allows a downstream statistics circuit  565  to generate statistics on a version of the output image of the SBIN circuit  550 . The version of the raw image data may be an unprocessed raw image data or a version that is adjusted by one or more circuit units such as the CROP circuit  510 , the PDP processing circuit  520 , the GOC circuit  530 , and the AFE circuit  540 . The statistics circuit  565  may be operatively coupled to the SBIN circuit and may include the RVC circuit  560 , the HIST generation circuit  570 , and the MMS circuit  580 . The down-sampling of the raw image data by the SBIN circuit  550  may include reducing the number of pixels in the output image compared to the input image. For example, each of the output pixel value may correspond to a plurality to input pixels in the raw image data. For example, for a down-sampling rate of eight to one, the SBIN circuit  550  down-samples two-pixel quads in the raw image data to a single value. The SBIN circuit  550  may also down-sample the raw image data using different methods. 
     A statistics circuit  565  may be coupled to the SBIN circuit  550  to receive the output image of the SBIN circuit  550  to generate various statistics of the output image. The statistics may represent the exposure level of the image sensor  202  that is connected to the auto-focus circuit  350 . The statistics circuit  565  may generate different types of statistics using different circuit units such as the HIST generation circuit  570  and the MMS circuit  580 . 
     The radial vignetting correction (RVC) circuit  560  may perform a vignetting correction to compensate for lens falloff before the image data is processed to generate any statistical datasets of the image data. An image sensor  202  may include a lens or another optical component reduces the light exposure of the image sensor near the edges such as at the corners. The RVC circuit  560  may apply gain to the pixel values at the edges or near the corner to compensate for the luminance fall-off. The RVC circuit  560  may apply a radial gain calculation, in which the pixel values are multiplied to different gain values that increase from the optical center of the input image. 
     After the output image of the SBIN circuit  550  is compensated for radial vignetting, one or more types of statistics data may be generated by different circuits. Example output datasets may include a statistical dataset that can be represented by a histogram and another statistical dataset that includes key metrics of the output image such as the minimum value, the maximum value, the sum of the values in the output image, and the sum of squares of the values within a subset of values such as within a rectangular window in the output image. The min max sum (MMS) circuit  580  may be a circuit that is designed to quickly generate those metrics. The histogram (HIST) generation circuit  570  generates a statistical dataset that can be represented by a color histogram. A color histogram provides a statistical distribution of color values in the output image of the SBIN circuit  550 . The color histogram may have a certain number of bins such as 64 bins. A bin may be a discretization level of a certain range of the color value. The HIST generation circuit  570  may count the number of pixels that belong to each bin in the output image of the SBIN circuit  550  and store each of the individual counts in a register for the access of other circuits. Both the histogram statistical dataset and the min max sum statistical dataset may represent the exposure level of the image sensor  202 . The control circuit  590 , based on the statistical datasets, may control the ISO, shutter speed and aperture size of the image sensor  202  to adjust the exposure level. 
     Example Auto-Focus Processes 
       FIG. 6  is a flowchart depicting an example process that may be carried out by an image signal processor (ISP) of an electronic device, according to an embodiment. The ISP may receive  610 , at an auto-focus circuit, raw image data from the image sensor. The auto-focus circuit of the ISP may determine  620  a phase shift among focus pixel values from the image sensor. 
     The determination of the phase shift may vary in different embodiments. In one embodiment, the determination of the phase shift may include identifying focus pixel values from the image data based on the known locations of the focus pixels. The determination of the phase shift may also include selecting, from the identified focus pixel values, a first focus pixel value corresponding to a first focus pixel with a first portion blocked from light exposure. The determination of the phase shift may further include selecting, from the identified focus pixel values, a second focus pixel value corresponding to a second focus pixel with a second portion blocked from light exposure. The second portion may be different from a corresponding portion blocked in the first focus pixel. The phase shift may be derived from a difference between the first and second focus pixel values. 
     In another embodiment, the determination of the phase shift may include selecting, from the identified focus pixel values, a focus pixel value corresponding to a focus pixel with a portion blocked from light exposure. The determination of the phase shift may also include calculating a corrected focus pixel value that estimates the focus pixel value of the focus pixel without the portion blocked. The calculating may be based on image pixels neighboring the focus pixel. The phase shift may be derived from a difference between the focus pixel value and the corrected focus pixel value. After the phase shift is determined, the ISP may generate  630  a focus signal based on the phase shift. The focus signal is sent to the image sensor to change the lens position. 
     The electronic device may include a single image sensor or multiple image sensors. In an embodiment that includes multiple image sensors, the ISP of the electronic device may include additional auto-focus circuits for processing raw image data from the image sensors. The ISP may receive  640 , at another auto-focus circuit that is separate from the first auto-focus circuit, the other raw image data from another image sensor when other pipelines of the ISP are processing the first raw image data. The first image sensor may be active while the other image sensor may be in standby. The other auto-focus circuit may process the other raw image data from the standby sensor when the statistics circuit is processing the raw image data from the active sensor. The other auto-focus circuit may generate  650  a second focus signal that causes the other image sensor to change a lens position of the other image sensor so that the standby image sensor may remain in focus during which the first image sensor is active. 
     The ISP may swap the active image sensor and the standby image sensor. The transition between two image sensors may be without delay because the lens positions and exposure levels of two image sensors are continuously adjusted even though one image sensor is in standby. The ISP may receive a command to switch from the first image sensor to the second image sensor. The command may be generated by a user action or may be generated by a processor (the ISP or another processor) of the electronic device. The ISP may activate the second image sensor by transmitting the raw image data of the second image sensor to other image processing pipelines of the ISP. The first auto-focus sensor may continue to receive the raw image data from the first image sensor that is now turned standby. The first auto-focus sensor may continue to adjust the lens position of the first image sensor when the second image sensor is active. 
     The ISP may receive  660 , in parallel with the auto-focus circuits and at a statistics circuit of the ISP that is separate from the auto-focus circuits, the raw image data captured by the image sensor. The statistic circuit performs various operations to collect statistics information associated with the raw image data. 
     In this disclosure, different image processing circuits such as the auto-focus circuits and the statistics circuit may receive and process data in parallel. Also, two image sensors may change lens positions in parallel when one of the image sensors is active. In parallel does not necessarily imply that actions must be performed simultaneously. For example, in parallel processing does not imply that the raw image data must be received by both the statistics circuit and the auto-focus circuit at the same time. In parallel may refer to a configuration that neither the auto-focus circuit nor the statistics circuit that is separate from the auto-focus circuit relies on the output of each other. Put differently, the auto-focus circuit and the statistics circuit may perform computation separately and independently. Also, in parallel does not necessarily imply that the raw image data received by the statistics circuit and by the auto-focus circuit are identical. In one embodiment, the statistics circuit may receive a first processed version of the raw image data and the auto-focus circuit may receive a second processed version of the raw image data of the same image sensor. Likewise, when the two image sensors change lens positions, in parallel does not imply that the two image sensors must change their respective lens position simultaneously. 
     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.