Patent Publication Number: US-11388348-B2

Title: Systems and methods for dynamic range compression in multi-frame processing

Description:
CROSS-REFERENCE TO RELATED APPLICATION AND PRIORITY CLAIM 
     This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 63/054,187 filed on Jul. 20, 2020, which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to image processing systems. More specifically, this disclosure relates to systems and methods for dynamic range compression in multi-frame processing. 
     BACKGROUND 
     Many mobile electronic devices, such as smart phones and tablet computers, include cameras that can be used to capture still and video images. While convenient, cameras on mobile electronic devices typically suffer from a number of shortcomings, including poor performance in low-light situations. Producing high-quality nighttime images or other images of dark scenes may require multi-frame processing in the Bayer domain. 
     SUMMARY 
     This disclosure provides systems and methods for dynamic range compression in multi-frame processing. 
     In a first embodiment, a method includes obtaining, using at least one processor of an electronic device, a high dynamic range (HDR) input Bayer image. The method also includes generating, using the at least one processor of the electronic device, a plurality of synthesized images at different exposure levels based on the input Bayer image. The method further includes fusing, using the at least one processor of the electronic device, the synthesized images to generate a fused image. The method also includes generating, using the at least one processor of the electronic device, a gain map based on the fused image. In addition, the method includes applying, using the at least one processor of the electronic device, a gain based on the gain map to the input Bayer image. 
     In a second embodiment, an electronic device includes at least one processing device configured to obtain an HDR input Bayer image. The at least one processing device is also configured to generate a plurality of synthesized images at different exposure levels based on the input Bayer image. The at least one processing device is further configured to fuse the synthesized images to generate a fused image. The at least one processing device is also configured to generate a gain map based on the fused image. In addition, the at least one processing device is configured to apply a gain based on the gain map to the input Bayer image. 
     In a third embodiment, a non-transitory machine-readable medium contains instructions that when executed cause at least one processor of an electronic device to obtain an HDR input Bayer image. The medium also contains instructions that when executed cause the at least one processor to generate a plurality of synthesized images at different exposure levels based on the input Bayer image. The medium further contains instructions that when executed cause the at least one processor to fuse the synthesized images to generate a fused image. The medium also contains instructions that when executed cause the at least one processor to generate a gain map based on the fused image. In addition, the medium contains instructions that when executed cause the at least one processor to apply a gain based on the gain map to the input Bayer image. 
     Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims. 
     Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “transmit,” “receive,” and “communicate,” as well as derivatives thereof, encompass both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. 
     Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device. 
     As used here, terms and phrases such as “have,” “may have,” “include,” or “may include” a feature (like a number, function, operation, or component such as a part) indicate the existence of the feature and do not exclude the existence of other features. Also, as used here, the phrases “A or B,” “at least one of A and/or B,” or “one or more of A and/or B” may include all possible combinations of A and B. For example, “A or B,” “at least one of A and B,” and “at least one of A or B” may indicate all of (1) including at least one A, (2) including at least one B, or (3) including at least one A and at least one B. Further, as used here, the terms “first” and “second” may modify various components regardless of importance and do not limit the components. These terms are only used to distinguish one component from another. For example, a first user device and a second user device may indicate different user devices from each other, regardless of the order or importance of the devices. A first component may be denoted a second component and vice versa without departing from the scope of this disclosure. 
     It will be understood that, when an element (such as a first element) is referred to as being (operatively or communicatively) “coupled with/to” or “connected with/to” another element (such as a second element), it can be coupled or connected with/to the other element directly or via a third element. In contrast, it will be understood that, when an element (such as a first element) is referred to as being “directly coupled with/to” or “directly connected with/to” another element (such as a second element), no other element (such as a third element) intervenes between the element and the other element. 
     As used here, the phrase “configured (or set) to” may be interchangeably used with the phrases “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of” depending on the circumstances. The phrase “configured (or set) to” does not essentially mean “specifically designed in hardware to.” Rather, the phrase “configured to” may mean that a device can perform an operation together with another device or parts. For example, the phrase “processor configured (or set) to perform A, B, and C” may mean a generic-purpose processor (such as a CPU or application processor) that may perform the operations by executing one or more software programs stored in a memory device or a dedicated processor (such as an embedded processor) for performing the operations. 
     The terms and phrases as used here are provided merely to describe some embodiments of this disclosure but not to limit the scope of other embodiments of this disclosure. It is to be understood that the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. All terms and phrases, including technical and scientific terms and phrases, used here have the same meanings as commonly understood by one of ordinary skill in the art to which the embodiments of this disclosure belong. It will be further understood that terms and phrases, such as those defined in commonly-used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined here. In some cases, the terms and phrases defined here may be interpreted to exclude embodiments of this disclosure. 
     Examples of an “electronic device” according to embodiments of this disclosure may include at least one of a smartphone, a tablet personal computer (PC), a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop computer, a netbook computer, a workstation, a personal digital assistant (PDA), a portable multimedia player (PMP), an MP3 player, a mobile medical device, a camera, or a wearable device (such as smart glasses, a head-mounted device (HMD), electronic clothes, an electronic bracelet, an electronic necklace, an electronic accessory, an electronic tattoo, a smart mirror, or a smart watch). Other examples of an electronic device include a smart home appliance. Examples of the smart home appliance may include at least one of a television, a digital video disc (DVD) player, an audio player, a refrigerator, an air conditioner, a cleaner, an oven, a microwave oven, a washer, a drier, an air cleaner, a set-top box, a home automation control panel, a security control panel, a TV box (such as SAMSUNG HOMESYNC, APPLETV, or GOOGLE TV), a smart speaker or speaker with an integrated digital assistant (such as SAMSUNG GALAXY HOME, APPLE HOMEPOD, or AMAZON ECHO), a gaming console (such as an XBOX, PLAYSTATION, or NINTENDO), an electronic dictionary, an electronic key, a camcorder, or an electronic picture frame. Still other examples of an electronic device include at least one of various medical devices (such as diverse portable medical measuring devices (like a blood sugar measuring device, a heartbeat measuring device, or a body temperature measuring device), a magnetic resource angiography (MRA) device, a magnetic resource imaging (MRI) device, a computed tomography (CT) device, an imaging device, or an ultrasonic device), a navigation device, a global positioning system (GPS) receiver, an event data recorder (EDR), a flight data recorder (FDR), an automotive infotainment device, a sailing electronic device (such as a sailing navigation device or a gyro compass), avionics, security devices, vehicular head units, industrial or home robots, automatic teller machines (ATMs), point of sales (POS) devices, or Internet of Things (IoT) devices (such as a bulb, various sensors, electric or gas meter, sprinkler, fire alarm, thermostat, street light, toaster, fitness equipment, hot water tank, heater, or boiler). Other examples of an electronic device include at least one part of a piece of furniture or building/structure, an electronic board, an electronic signature receiving device, a projector, or various measurement devices (such as devices for measuring water, electricity, gas, or electromagnetic waves). Note that, according to various embodiments of this disclosure, an electronic device may be one or a combination of the above-listed devices. According to some embodiments of this disclosure, the electronic device may be a flexible electronic device. The electronic device disclosed here is not limited to the above-listed devices and may include new electronic devices depending on the development of technology. 
     In the following description, electronic devices are described with reference to the accompanying drawings, according to various embodiments of this disclosure. As used here, the term “user” may denote a human or another device (such as an artificial intelligent electronic device) using the electronic device. 
     Definitions for other certain words and phrases may be provided throughout this patent document. Those of ordinary skill in the art should understand that in many if not most instances, such definitions apply to prior as well as future uses of such defined words and phrases. 
     None of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle. Use of any other term, including without limitation “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” or “controller,” within a claim is understood by the Applicant to refer to structures known to those skilled in the relevant art and is not intended to invoke 35 U.S.C. § 112(f). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts: 
         FIG. 1  illustrates an example network configuration including an electronic device according to this disclosure; 
         FIG. 2  illustrates an example process for dynamic range compression in multi-frame processing according to this disclosure; 
         FIG. 3  illustrates an example input image and an example output image in accordance with this disclosure; 
         FIG. 4  illustrates an example Luma-based process for dynamic range compression in accordance with this disclosure; 
         FIG. 5  illustrates an example lookup table generated by a compression operation in accordance with this disclosure; 
         FIG. 6  illustrates an example lookup table generated by a saturation operation in accordance with this disclosure; 
         FIG. 7  illustrates an example lookup table generated by a highlight operation in accordance with this disclosure; 
         FIG. 8  illustrates an example Gamma-S curve operation in accordance with this disclosure; 
         FIG. 9  illustrates an example YUV-based process for dynamic range compression in accordance with this disclosure; 
         FIG. 10  illustrates example short, medium, and long exposed Bayer images in accordance with this disclosure; 
         FIG. 11  illustrates an example image signal processing (ISP) pipeline operation of  FIG. 9  in accordance with this disclosure; 
         FIG. 12  illustrates an example inverse ISP pipeline operation of  FIG. 9  in accordance with this disclosure; 
         FIG. 13  illustrates an example output of an image fusion operation in accordance with this disclosure; and 
         FIG. 14  illustrates an example method for dynamic range compression in accordance with this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIGS. 1 through 14 , discussed below, and the various embodiments of this disclosure are described with reference to the accompanying drawings. However, it should be appreciated that this disclosure is not limited to these embodiments and all changes and/or equivalents or replacements thereto also belong to the scope of this disclosure. 
     As noted above, many mobile electronic devices, such as smart phones and tablet computers, include cameras that can be used to capture still and video images. While convenient, cameras on mobile electronic devices typically suffer from a number of shortcomings, including poor performance in low-light situations. Producing high-quality nighttime images or other images of dark scenes may require multi-frame processing in the Bayer domain. Unfortunately, the result of combining multiple image frames at different exposure levels is a high-dynamic range (HDR) image that needs to be compressed back to a displayable range. 
     This disclosure provides systems and methods for dynamic range compression in multi-frame processing. As described in more detail below, a Bayer/linear mapping system can be used to produce high-quality images of scenes. The systems and methods disclosed here may generate multiple nonlinear Luma, YUV, or other images, and tone mapping operations can be performed by manipulating the images. Once processing is completed, a resulting image may be converted back into the Bayer/linear domain. The resulting output is a displayable-range Bayer/linear image with minimal contrast loss due to compression. 
     Depending on the implementation, a gain map can be generated to guide the compression in the original Bayer domain based on a fused image. For example, the gain map may determine a pixel-wise compression level that can be applied to the original Bayer image to yield a toned output Bayer image. When Luma images are used, a family of gamma-S curves may be applied to the Luma images, offering control of a compression-contrast trade-off. One possibility here involves the use of the half-scale Luma domain for guidance on compression, which may be used to provide an artifact-free toned Bayer output. When LUV images are used, an image signal processing (ISP) pipeline may be used to convert Bayer images into multiple YUV images of different exposures. A tone-mapped Bayer image is not sensitive to the parameter selection of the ISP pipeline, and the scales used to scale a Bayer image to different exposure levels may be adaptive to scene statistics. For instance, the highest scale may be increased if the overall scene brightness level is low, and the lowest scale may be forbidden from exceeding a threshold if the overall scene brightness level is high. Color-awareness may be used to help preserve color saturation in gain map generation, and bilinear interpolation may be applied in a demosaic operation to help avoid staircase artifacts in the gain map generation. The gain map applied on a Bayer image may be determined as the weighted average of three color planes to avoid chroma noise amplification. 
       FIG. 1  illustrates an example network configuration  100  including an electronic device according to this disclosure. The embodiment of the network configuration  100  shown in  FIG. 1  is for illustration only. Other embodiments of the network configuration  100  could be used without departing from the scope of this disclosure. 
     According to embodiments of this disclosure, an electronic device  101  is included in the network configuration  100 . The electronic device  101  can include at least one of a bus  110 , a processor  120 , a memory  130 , an input/output (I/O) interface  150 , a display  160 , a communication interface  170 , or a sensor  180 . In some embodiments, the electronic device  101  may exclude at least one of these components or may add at least one other component. The bus  110  includes a circuit for connecting the components  120 - 180  with one another and for transferring communications (such as control messages and/or data) between the components. 
     The processor  120  includes one or more of a central processing unit (CPU), an application processor (AP), or a communication processor (CP). The processor  120  is able to perform control on at least one of the other components of the electronic device  101  and/or perform an operation or data processing relating to communication. In some embodiments, the processor  120  can be a graphics processor unit (GPU). As described in more detail below, the processor  120  can perform dynamic range compression in multi-frame processing. For example, the processor  120  can obtain an HDR input Bayer image, generate a plurality of synthesized images at different exposure levels based on the input Bayer image, fuse the synthesized images to generate a fused image, generate a gain map based on the fused image, and apply a gain based on the gain map to the input Bayer image. 
     The memory  130  can include a volatile and/or non-volatile memory. For example, the memory  130  can store commands or data related to at least one other component of the electronic device  101 . According to embodiments of this disclosure, the memory  130  can store software and/or a program  140 . The program  140  includes, for example, a kernel  141 , middleware  143 , an application programming interface (API)  145 , and/or an application program (or “application”)  147 . At least a portion of the kernel  141 , middleware  143 , or API  145  may be denoted an operating system (OS). 
     The kernel  141  can control or manage system resources (such as the bus  110 , processor  120 , or memory  130 ) used to perform operations or functions implemented in other programs (such as the middleware  143 , API  145 , or application  147 ). The kernel  141  provides an interface that allows the middleware  143 , the API  145 , or the application  147  to access the individual components of the electronic device  101  to control or manage the system resources. The application  147  includes one or more applications for image capture and image processing as discussed below. These functions can be performed by a single application or by multiple applications that each carry out one or more of these functions. The middleware  143  can function as a relay to allow the API  145  or the application  147  to communicate data with the kernel  141 , for instance. A plurality of applications  147  can be provided. The middleware  143  is able to control work requests received from the applications  147 , such as by allocating the priority of using the system resources of the electronic device  101  (like the bus  110 , the processor  120 , or the memory  130 ) to at least one of the plurality of applications  147 . The API  145  is an interface allowing the application  147  to control functions provided from the kernel  141  or the middleware  143 . For example, the API  145  includes at least one interface or function (such as a command) for filing control, window control, image processing, or text control. 
     The I/O interface  150  serves as an interface that can, for example, transfer commands or data input from a user or other external devices to other component(s) of the electronic device  101 . The I/O interface  150  can also output commands or data received from other component(s) of the electronic device  101  to the user or the other external device. 
     The display  160  includes, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a quantum-dot light emitting diode (QLED) display, a microelectromechanical systems (MEMS) display, or an electronic paper display. The display  160  can also be a depth-aware display, such as a multi-focal display. The display  160  is able to display, for example, various contents (such as text, images, videos, icons, or symbols) to the user. The display  160  can include a touchscreen and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a body portion of the user. 
     The communication interface  170 , for example, is able to set up communication between the electronic device  101  and an external electronic device (such as a first electronic device  102 , a second electronic device  104 , or a server  106 ). For example, the communication interface  170  can be connected with a network  162  or  164  through wireless or wired communication to communicate with the external electronic device. The communication interface  170  can be a wired or wireless transceiver or any other component for transmitting and receiving signals, such as images or videos. 
     The wireless communication is able to use at least one of, for example, long term evolution (LTE), long term evolution-advanced (LTE-A), 5th generation wireless system (5G), millimeter-wave or 60 GHz wireless communication, Wireless USB, code division multiple access (CDMA), wideband code division multiple access (WCDMA), universal mobile telecommunication system (UMTS), wireless broadband (WiBro), or global system for mobile communication (GSM), as a cellular communication protocol. The wired connection can include, for example, at least one of a universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). The network  162  or  164  includes at least one communication network, such as a computer network (like a local area network (LAN) or wide area network (WAN)), Internet, or a telephone network. 
     The electronic device  101  further includes one or more sensors  180  that can meter a physical quantity or detect an activation state of the electronic device  101  and convert metered or detected information into an electrical signal. For example, one or more sensors  180  can include one or more cameras or other imaging sensors for capturing images of scenes. The sensor(s)  180  can also include one or more buttons for touch input, a gesture sensor, a gyroscope or gyro sensor, an air pressure sensor, a magnetic sensor or magnetometer, an acceleration sensor or accelerometer, a grip sensor, a proximity sensor, a color sensor (such as a red green blue (RGB) sensor), a bio-physical sensor, a temperature sensor, a humidity sensor, an illumination sensor, an ultraviolet (UV) sensor, an electromyography (EMG) sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, an infrared (IR) sensor, an ultrasound sensor, an iris sensor, or a fingerprint sensor. The sensor(s)  180  can further include an inertial measurement unit, which can include one or more accelerometers, gyroscopes, and other components. In addition, the sensor(s)  180  can include a control circuit for controlling at least one of the sensors included here. Any of these sensor(s)  180  can be located within the electronic device  101 . 
     The first external electronic device  102  or the second external electronic device  104  can be a wearable device or an electronic device-mountable wearable device (such as an HMD). When the electronic device  101  is mounted in the electronic device  102  (such as the HMD), the electronic device  101  can communicate with the electronic device  102  through the communication interface  170 . The electronic device  101  can be directly connected with the electronic device  102  to communicate with the electronic device  102  without involving with a separate network. The electronic device  101  can also be an augmented reality wearable device, such as eyeglasses, that include one or more cameras. 
     The first and second external electronic devices  102  and  104  and the server  106  each can be a device of the same or a different type from the electronic device  101 . According to certain embodiments of this disclosure, the server  106  includes a group of one or more servers. Also, according to certain embodiments of this disclosure, all or some of the operations executed on the electronic device  101  can be executed on another or multiple other electronic devices (such as the electronic devices  102  and  104  or server  106 ). Further, according to certain embodiments of this disclosure, when the electronic device  101  should perform some function or service automatically or at a request, the electronic device  101 , instead of executing the function or service on its own or additionally, can request another device (such as electronic devices  102  and  104  or server  106 ) to perform at least some functions associated therewith. The other electronic device (such as electronic devices  102  and  104  or server  106 ) is able to execute the requested functions or additional functions and transfer a result of the execution to the electronic device  101 . The electronic device  101  can provide a requested function or service by processing the received result as it is or additionally. To that end, a cloud computing, distributed computing, or client-server computing technique may be used, for example. While  FIG. 1  shows that the electronic device  101  includes the communication interface  170  to communicate with the external electronic device  104  or server  106  via the network  162  or  164 , the electronic device  101  may be independently operated without a separate communication function according to some embodiments of this disclosure. 
     The server  106  can include the same or similar components  110 - 180  as the electronic device  101  (or a suitable subset thereof). The server  106  can support to drive the electronic device  101  by performing at least one of operations (or functions) implemented on the electronic device  101 . For example, the server  106  can include a processing module or processor that may support the processor  120  implemented in the electronic device  101 . In some embodiments, the server  106  can perform dynamic range compression in multi-frame processing. For example, the server  106  can obtain an HDR input Bayer image, generate a plurality of synthesized images at different exposure levels based on the input Bayer image, fuse the synthesized images to generate a fused image, generate a gain map based on the fused image, and apply a gain based on the gain map to the input Bayer image. 
     Although  FIG. 1  illustrates one example of a network configuration  100  including an electronic device  101 , various changes may be made to  FIG. 1 . For example, the network configuration  100  could include any number of each component in any suitable arrangement. In general, computing and communication systems come in a wide variety of configurations, and  FIG. 1  does not limit the scope of this disclosure to any particular configuration. Also, while  FIG. 1  illustrates one operational environment in which various features disclosed in this patent document can be used, these features could be used in any other suitable system. 
       FIG. 2  illustrates an example process  200  for dynamic range compression in multi-frame processing according to this disclosure. For ease of explanation, the process  200  is described as being implemented using the electronic device  101  shown in  FIG. 1 . However, the process  200  could be implemented in any other suitable electronic device and in any suitable system, such as by the server  106 . 
     As shown in  FIG. 2 , the process  200  receives, generates, or otherwise obtains an input of a linear image  210 . The input linear image  210  may be a frame, single image, multiple images, or a video stream. In the examples that follow, the linear image  210  is assumed to be a 16-bit Bayer image, although other types of input linear images  210  may be received and processed. The process  200  includes a domain conversion operation  220 , which generally operates to convert the input Bayer image  210  into another image domain for further processing. The other image domain may be, for example, the Luma domain, the YUV domain, or any other suitable image domain. The domain conversion operation  220  may support any suitable technique for converting image data between domains. 
     An image synthesis operation  230  generally operates to produce multiple synthesized images at different exposure levels based on the input Bayer image  210  (or, more specifically, the converted version of the input Bayer image  210 ). The image synthesis operation  230  may use any suitable technique to synthesize images at different exposure levels based on an input Bayer image. For example, in some embodiments, the image synthesis operation  230  may use one or more lookup tables as described below to generate the synthesized images. An image fusion operation  240  generally operates to receive the synthesized images and to fuse the synthesized images into a fused or blended image. The image fusion operation  240  may use any suitable technique to fuse synthesized images. For instance, in some embodiments, the image fusion operation  240  may use a pyramid blending technique to fuse the synthesized images. 
     A gain map operation  250  generally operates to receive the converted version of the input Bayer image  210  and the fused image in order to generate a gain map. The gain map operation  250  may use any suitable technique to generate a gain map. For example, in some embodiments, the gain map operation  250  may transform the fused image into a first RGB image, convert the input Bayer image into a second RGB image using a demosaic function to compute red, green, and blue plane gain maps, and generate the gain map based on the first RGB image and the red, green, and blue plane gain maps. A gain apply operation  260  generally operates to apply gains as represented within the gain map to the input Bayer image  210  in order to generate an output Bayer image  270 . As part of this process, the gain apply operation  260  can reduce the number of bits representing the image data in the output Bayer image  270 . For instance, the gain apply operation  260  may convert 16-bit values in the input Bayer image  210  into 12-bit values in the output Bayer image  270 . 
     Note that the functions and other operations described above with reference to  FIG. 2  can be implemented in an electronic device  101 ,  102 ,  104 , server  106 , or other device in any suitable manner. For example, in some embodiments, the operations described above can be implemented or supported using one or more software applications or other software instructions that are executed by at least one processor  120  of a device. In other embodiments, at least some of the operations described above can be implemented or supported using dedicated hardware components. In general, the operations described above can be performed using any suitable hardware or any suitable combination of hardware and software/firmware instructions. 
     Although  FIG. 2  illustrates one example of a process  200  for dynamic range compression in multi-frame processing, various changes may be made to  FIG. 2 . For example, each operation  220 ,  230 ,  240 ,  250 ,  260  may occur any number of times as needed or desired in order to process input images and generate output images. 
       FIG. 3  illustrates an example input image  310  and an example output image  320  in accordance with this disclosure. The input image  310  may represent the input Bayer image  210  of  FIG. 2 , and the output image  320  may represent the output Bayer image  270  of  FIG. 2 . As can be seen here, the process  200  generally operates to provide dynamic range compression of the input image  310  in order to produce a more pleasing output image  320 . Among other things, the image contents of the output image  320  are much clearer and easier to see compared to the input image  310 . As noted above, part of the process  200  may include converting 16-bit values or other values of the input image  310  into 12-bit values or other values of the output image  320 . 
     Although  FIG. 3  illustrates one example of an input image  310  and one example of an output image  320 , various changes may be made to  FIG. 3 . For example, the images  310  and  320  shown here are merely meant to illustrate one example of the type of result that might be obtained using the process  200 . Of course, image contents can vary widely based on the scenes, so the results obtained using the process  200  can also vary widely. 
       FIG. 4  illustrates an example Luma-based process  400  for dynamic range compression in accordance with this disclosure. More specifically,  FIG. 4  illustrates how the process  200  may be implemented when processing images in the Luma domain. For ease of explanation, the process  400  is described as being implemented using the electronic device  101  shown in  FIG. 1 . However, the process  400  could be implemented in any other suitable electronic device and in any suitable system, such as by the server  106 . 
     As shown in  FIG. 4 , the input Bayer image  210  is provided to the domain conversion operation  220 , which converts the input Bayer image  210  into Luma image  405 . In some embodiments, to convert an image from the Bayer domain (W×H) to the Luma domain (W/2×H/2), the following fixed conversion may be applied, where R is based on red pixels, G is based on green pixels, and B is based on blue pixels in the image  210 :
 
Luma Domain=0.2126× R+ 0.7152×(aggregated  G )/2+0.0722× B   (1)
 
     After the input Bayer image  210  is converted into the Luma domain, multiple synthesized images may be generated, where at least some of the synthesized images have different exposure levels. For example, first and second synthesized images may be generated, where the first image has a longer exposure and is therefore brighter and the second image has a shorter exposure and is therefore darker (at least relative to the first image). In this example, to generate the synthesized images, a long lookup table (LUT)  420  and a short LUT  435  may be dynamically created and applied to Luma image  405  through apply operations  440  and  445 , respectively. The output of the apply operation  440  is a long Luma image  405 -L, and the output of the apply operation  445  is a short Luma image  405 -S. 
     Once the Luma images  405 -L and  405 -S are generated, the electronic device  101  may perform a series of operations to support fusing, such as pyramid blending, of the Luma images  405 -L and  405 -S. An example of the pyramid blending process will be explained in more detail below. The output of the pyramid blending process may be a single blended image  481 , which may be said to represent a fused image. At this point, any number of post-processing operations may be performed on the blended image  481 . For instance, the electronic device  101  may perform a contrast limited adaptive histogram equalization (CLAHE) operation  490  to improve the contrast of the blended image  481  and generate a processed blended image  491 . Other and/or additional image processing techniques may also be applied to the blended image  481  or  491 . The gain map operation  250  may then generate a gain map  251 , such as by dividing the blended image  481  or  491  by the Luma image  405 . The gain operation  260  then applies gains based on the gain map  251  to the input Bayer image  210 . 
     The following now describes example operations that may be performed by the electronic device  101  to create the long LUT  420  and the short LUT  435  of  FIG. 4 . Note, however, that other suitable techniques may be used here to generate lookup tables. In this example, a global compression operation  410  compresses the input Bayer image  210  (such as by reducing 16-bit values to 12-bit values) by dynamically generating a global compression LUT  411  based on the input Bayer image  210 . For example, the global compression operation  410  may calculate the global compression LUT  411  using the Equation (2) below, where the listed variables will be explained in more detail below. 
                     D   ⁡     (   I   )       =         (       D   max     -     D   min       )     *         log   ⁡     (     I   +   τ     )       -     log   ⁡     (       I   min     +   τ     )             log   ⁡     (       I   max     +   τ     )       -     log   ⁡     (       I   min     +   τ     )             +     D   min               (   2   )               
To solve for τ, the following equation may be used:
 
                   k   =         log   ⁡     (       I   ave     +   τ     )       -     log   ⁡     (       I   min     +   τ     )             log   ⁡     (       I   max     +   τ     )       -     log   ⁡     (       I   min     +   τ     )                   (   3   )               
where k is computed as:
 
 k=A×B   (2 log I     ave     -log I     min     -log I     max     )/(log I     max     -log I     min     )   (4)
 
       FIG. 5  illustrates an example lookup table  411  generated by a compression operation in accordance with this disclosure. More specifically, the lookup table  411  in  FIG. 5  may be generated by the global compression operation  410  of  FIG. 4 . As shown in  FIG. 5 , each 16-bit pixel value in the input Bayer image  210  may be mapped to a corresponding 12-bit pixel value. 
     With reference to Equations (2), (3), and (4) above, D min  and D max  may be set based on the bitwise range. In the example global compression LUT  411  of  FIG. 5 , the desired output is a 12-bit pixel value, so D min  is 0 and D max  is 4,096. Similarly, I min  and I max  represent the minimum and maximum values of the bitwise range of the input Bayer image  210 . Also, A and B of Equation (4) represent a variety of parameters that may be pre-configured, chosen by a user, adjusted, dynamically determined, or otherwise determined in any suitable manner. For example, an electronic device  101  may determine a parameter or combination of parameters to apply or adjust based on the input. Example parameters here may include or be related to brightness, contrast, a certain focus area, or any other suitable image characteristic(s). 
     Due to the compression applied to the input Bayer image  210  in the compression operation  410 , there may be a loss in contrast in the resulting compressed image. Accordingly, a global contrast operation  415  may receive or otherwise obtain the compressed image generated by the compression operation  410  and increase the contrast of the compressed image. In some embodiments, the contrast operation  415  may be a stretching operation, which could be implemented using the recursive binary cut implementation of the Histogram Adjustment based Linear to Equalized Quantizer (HALEQ). The result of the contrast operation  415  is a global contrast LUT  412 . 
     The long LUT  420  may then be generated using the global compression LUT  411  and the global contrast LUT  412 . In some embodiments, the long LUT  420  may be formed as a concatenation of the global compression LUT  411  and the global contrast LUT  412 . This can be expressed as:
 
Long LUT( x )=Global contrast LUT(Global compression LUT( x ))  (5)
 
     In some embodiments, a compression operation (such as the operation  410 ) may cause some or all white areas of a short-exposure image to have a grayish color due to over-compression of those areas. In  FIG. 4 , a short LUT  435  may be generated and used to bring gray levels back to the saturation level. The short LUT  435  may be generated by performing a saturation operation  425  and a highlight operation  430 . In the saturation operation  425 , the long LUT  420  is analyzed to create a saturation LUT  413 .  FIG. 6  illustrates an example lookup table  413  generated by a saturation operation in accordance with this disclosure. More specifically, the lookup table  413  in  FIG. 6  may be generated by the saturation operation  425  of  FIG. 4 . As shown in  FIG. 6 , both the inputs and the outputs are 12-bit values. In some embodiments, the saturation LUT  413  may be generated using the following equations:
 
 Pt _half=Long LUT( D   max /2)  (6)
 
 Pt _full=Long LUT( D   max )  (7)
 
     In the highlight operation  430 , the input Bayer image  210  is stretched using an S-shaped curve. The resulting curve is used as a highlight contrast LUT  414 .  FIG. 7  illustrates an example lookup table  414  generated by a highlight operation in accordance with this disclosure. More specifically, the lookup table  414  in  FIG. 7  may be generated by the highlight operation  430  of  FIG. 4 . As shown in  FIG. 7 , the highlight contrast LUT  414  may have an S-shaped curve. In some embodiments, the highlight contrast LUT  414  may be generated using the following operation, which will be explained in more detail below:
 
Gamma-S curve LUT(x;1.6,8.5,8.5)  (8)
 
     After the saturation LUT  413  and the highlight contrast LUT  414  have been generated, a weighted blending operation  431  can be performed to generate the short LUT  435 . In some embodiments, the blending operation  431  may be expressed as:
 
Short LUT( x )=Saturation LUT(Long LUT(Highlight contrast LUT( x )))  (9)
 
     As noted above, the apply operations  440  and  445  can be performed using the long LUT  420  and the short LUT  435  to generate the short Luma image  405 -S and the long Luma image  405 -L. 
     In order to support pyramid blending of the short Luma image  405 -S and the long Luma image  405 -L, a pre-pyramid short operation  450  is used to generate a pre-pyramid short LUT  451 , and a pre-pyramid long operation  455  is used to generate a pre-pyramid long LUT  456 . In some embodiments, the pre-pyramid short LUT  451  can be generated using an equation such as:
 
Gamma-S curve LUT(x;1.2,9.5,−9.0)  (10)
 
Also, in some embodiments, the pre-pyramid long LUT  456  can be generated using an equation such as:
 
Gamma-S curve LUT(x;1.1,4.5,1.0)  (11)
 
     Apply LUT operations  460  and  465  respectively apply the pre-pyramid short LUT  451  to the short Luma image  405 -S and the pre-pyramid long LUT  456  to the long Luma image  405 -L. This results in the generation of a pre-pyramid short image  461  and a pre-pyramid long image  466 . A pyramid blending operation  480  then blends the pre-pyramid short image  461  and the pre-pyramid long image  466  to produce the blended image  481 . 
       FIG. 8  illustrates an example Gamma-S curve operation in accordance with this disclosure. A Gamma-S curve is generated by mixing together multiple sigmoid-gamma combination curves, each of which is generated by the following equations: 
                       (       1     1   +     e     -     (         x   ^     ⁢     s   i       -       s   i     /   2       )             -   m     )       M   -   m       ,           ⁢       s   i     ≥   0             (   12   )                     Log   ⁡     (       1         x   ^     ⁡     (     m   -   M     )       +   M       -   1     )         s   i       +     1   2       ,           ⁢     s   i                             &lt;   0           (   13   )               wher   ⁢     e:                               i   =   dark     ,   bright                           m   =     1     1   +     exp   ⁡     (       s   i     2     )                                   M   =     1     1   +     exp   ⁡     (       -     s   i       2     )                                     x   ^     =     x     1     inv   ⁢   _   ⁢   gamma                                 
and s i  is the parameter that controls the shape of the curve i. In some embodiments, each curve i can be controlled to focus on the dark and bright area of an image, respectively. The Gamma-S curve is therefore parameterized by inv_gamma and the s-parameters for the dark and bright areas:
 
Gamma-S curve LUT(x;inv_gamma,s_dark,s_bright)  (14)
 
     Returning to  FIG. 4 , an initial CLAHE operation  485  may be used to generate an initial CLAHE LUT  486 . For example, the initial CLAHE operation  485  may generate the initial CLAHE LUT  486  using an equation such as:
 
Gamma-S curve LUT(x;1.2,4.5,1.4) 1.2   (15)
 
     As noted above, the contrast limited adaptive histogram equalization operation  490  may be performed to improve the contrast of the blended image  481 . Here, the CLAHE operation  490  obtains the blended image  481 , applies the initial CLAHE LUT  486  to the blended image  481 , and generates the image  491 . 
     Note that the functions and other operations described above with reference to  FIGS. 4 through 8  can be implemented in an electronic device  101 ,  102 ,  104 , server  106 , or other device in any suitable manner. For example, in some embodiments, the operations described above can be implemented or supported using one or more software applications or other software instructions that are executed by at least one processor  120  of a device. In other embodiments, at least some of the operations described above can be implemented or supported using dedicated hardware components. In general, the operations described above can be performed using any suitable hardware or any suitable combination of hardware and software/firmware instructions. 
     Although  FIGS. 4 through 8  illustrate one example of a Luma-based process  400  for dynamic range compression and related details, various changes may be made to  FIGS. 4 through 8 . For example, each operation in  FIG. 4  may occur any number of times as needed or desired in order to process input images and generate output images. Also, it may be possible to generate and fuse more than two synthetic images. In addition, the graphs shown in  FIGS. 5 through 8  are for illustration only and can vary widely based on the image data being processed. 
     Note that in some cases, a single digital image captured by a mobile device may be insufficient to provide scene details in an HDR image. One approach to render an HDR image is to merge multiple different-exposed low dynamic range (LDR) images in a linear domain (Bayer domain). The fused Bayer image records the full dynamic range of the scene using high bit depth. In order to display the fused Bayer image on an LDR device, tone mapping techniques may be used to compress the fused Bayer image from high bit depth to a standard/displayable bit depth. However, many global or local tone mapping approaches directly applied in the Bayer domain are not adaptive to scene contents and may lead to various visual artifacts, such as flat effects, dark halos, and inconsistencies. Some embodiments of this disclosure therefore support a Bayer tone mapping system that may generate multiple nonlinear YUV images based on a fused Bayer image, perform tone mapping by manipulating the YUV images, and convert the YUV images to a compressed Bayer image. The process of converting the Bayer image to the YUV images may not be sensitive to parameter selections. Accordingly, the approaches disclosed here may be generalized to large numbers of datasets without specific metadata. 
       FIG. 9  illustrates an example YUV-based process  900  for dynamic range compression in accordance with this disclosure. For ease of explanation, the process  900  is described as being implemented using the electronic device  101  shown in  FIG. 1 . However, the process  900  could be implemented in any other suitable electronic device and in any suitable system, such as by the server  106 . 
     In general, a linear input image (such as an input Bayer image  210 ) may be used to generate synthesized images, such as in the manner described above with reference to  FIG. 4 . The synthesized images may be converted into the YUV domain and fused, and the fused image may be converted back into the original linear domain as a linear image for use in computing a gain map. Gains may then be applied to the original linear input image based on the gain map. 
     As shown in  FIG. 9 , the electronic device  101  may receive or otherwise obtain an input Bayer image I 16  and perform a scaling computation operation  910 . The scaling computation operation  910  generally operates to identify the scales to be used to scale the input Bayer image to different exposure levels, such as based on scene statistics. For instance, the highest scale may be increased if the overall scene brightness level is low, and the lowest scale may be forbidden from exceeding a threshold if the overall scene brightness level is high. 
     The input Bayer image I 16  is scaled and clipped by one or more operations  920  using the identified scales to simulate different-exposed image data. The operations  920  can also reduce the size of the values of the input Bayer image, such as by reducing 16-bit values to 12-bit values. In the example of  FIG. 9 , the operations  920  generate three different-exposed Bayer images, such as a short-exposed image I 1  (scale S 1 ), a medium-exposed image I 2  (scale S 2 ), and a long-exposed image I 3  (scale S 3 ). In some embodiments, the following equation may be used to generate the images:
 
 I   i =min( I   16   *S   i ,2 12 −1), i= 1,2,3  (16)
 
Note that while multiple operations  920  are shown here (one for each synthesized image), the same operation may be repeated sequentially with different parameters to generate the synthesized images.
 
     Instead of using fixed scales for all scenes to generate different exposure levels, the scales identified by the operation  910  can be adapted based on scene statistics. Various considerations may drive image optimization. For example, dark regions of a scene may generally originate from one or more long-exposed images, and it may be desirable to increase the scale value if the overall scene brightness level is low so that dark region details are improved in the combined output. Bright/saturated regions may generally originate from one or more short-exposed images, and a cap may be placed on the scale value to avoid losing saturation details. 
     One way to analyze scene brightness may be to compute the histogram of luminance of an auto-exposed image. The auto-exposed image may be generated by passing the clipped input Bayer image (i.e., I 1 , I 2 , . . . , I n ) through an ISP pipeline operation  930 . Once the histogram is known, the electronic device  101  may obtain the histogram&#39;s cumulative density function (CDF), where the CDF may range from 0 to 1. The value at which the CDF is equal to 0.5 may provide the median brightness level (histMed). A scale value of the long exposed image may be modulated using this value, such as in the following manner:
 
 S   m =(th base −min(max(histMed,th min ),th max ))/th min   (17)
 
 S   3   ′=S   3   *S   m   (19)
 
In some embodiments, based on observations of input data, th base  may be set to 100, th min  may be set to 40, and th max  may be set to 60. With these parameters, S m  may range from 1 to 1.5. The value of S m  may be used as a multiplier to update an initial scale value for long-exposed image (S 3 =4). Similarly, histMax can be defined as a value at which the CDF is nearly at 1 (such as 0.999), and histMax can be used along with S m  to update the scale value of the short exposed image, such as in the following manner:
 
                     S   1   ′     =       S   1     *     min   ⁡     (       max   ⁡     (       S   m     ,   1     )       ,       2   ⁢   5   ⁢   6       h   ⁢   i   ⁢   s   ⁢   t   ⁢   Max         )                 (   19   )               
where the initial value of S 1  is 0.0625.
 
     After updating the scale values of the short- and long-exposed images, the electronic device  101  may update the scale of the medium exposed-image in order to position the medium exposure in the middle of the range defined by the short and long exposures. For example, geometric averaging may be used to determine the updated scale as follows:
 
 S   2 ′=√{square root over ( S   1   ′S   3 ′)}  (20)
 
       FIG. 10  illustrates example short, medium, and long exposed Bayer images in accordance with this disclosure. More specifically,  FIG. 10  illustrates an example short exposed Bayer image  1010 , medium exposed Bayer image  1020 , and long exposed Bayer image  1030 . These images  1010 ,  1020 , and  1030  may be generated by the operations  920  based on the input Bayer image I 16 . 
     The ISP pipeline operations  930  are performed to convert the clipped and scaled short, medium, and long Bayer images S 1 , S 2 , and S 3  into the YUV domain. Again, note that while multiple operations  930  are shown here (one for each synthesized image), the same operation may be repeated sequentially with different parameters to generate the converted images.  FIG. 11  illustrates an example ISP pipeline operation  930  of  FIG. 9  in accordance with this disclosure. As shown in  FIG. 11 , a demosaic operation  931  converts an image, such as from a 12-bit Bayer format to a 12-bit RGB format. As is known, each two-by-two cell in a Bayer image S 1 , S 2 , or S 3  contains two green samples, one blue sample, and one red sample. The output RGB image is half the size of the Bayer image, where the output RGB&#39;s green plane averages the green samples and where the red and blue planes are interpolated red and blue samples. Interpolation coefficients may be based on the distance between observed samples and the interpolated locations. In some cases, the following equations may be used to convert each Bayer image S 1 , S 2 , and S 3  into the RGB format: 
     
       
         
           
             
               
                 
                   
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     In a dynamic range control (DRC) operation  932 , a DRC lookup table having gain values may be applied to the input RGB images, such as by applying high gain on pixels of low intensity and low or no gain on pixels of high intensity. Here, gain values may be fetched from the lookup table based on the luminance values. In some cases, the following equations may be used to perform the DRC operation  932 :
 
 Y   p= 0.25 *R   p +0.5 *G   p +0.25 *B   p   (24)
 
 g   p   =DRC ( Y   p )  (25)
 
     The output of the DRC operation  932  may be the product of the input RGB image and its corresponding gain, such as {tilde over (R)} p =R p *g p , {tilde over (B)} p =B p *g p , {tilde over (G)} p =G p *g p . 
     A color correction operation  933  applies a color correction matrix (CCM) to the output of the DRC operation  932 . In some embodiments, the color correction operation  933  may be expressed as a 3×3 matrix that transforms camera RGB values into RGB values suitable for viewing on a display, which may be expressed as: 
     
       
         
           
             
               
                 
                   
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     A nonlinear Gamma correction operation  934  simulates how light and color are perceived by the human eye. For example, the Gamma correction operation  934  may compress input RGB images from 12-bits to 8-bits, such as by using an equation of: 
     
       
         
           
             
               
                 
                   
                     
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     An RGB-to-YUV conversion operation  935  converts the 8-bit RGB images from the RGB domain to the YUV domain. 
     Outputs of the ISP pipeline operations  930  are provided to an image fusion and tone mapping operation  940 , which fuses the YUV domain images and performs tone mapping. Note that Bayer domain tone mapping may easily clip the darker details of an image, which may lead to a dark halo around an intensity boundary. Here, the tone mapping occurs in the YUV domain in which gamma-corrected images are used, which can help avoid dark halos and related issues. Also, Bayer domain tone mapping may directly compress Bayer data using a global curve, and the result of the global curve may appear flat locally. Here, the tone mapping and pyramid blending occur in the YUV domain, which may result in local tone mapping and the preservation of local contrast. In addition, it is possible for multiple images to capture the same scene but result in vastly different tone-mapped outputs. Bayer domain tone mapping uses a histogram of the Bayer data to choose parameters, but the histogram distribution of the Bayer data may not be reliable if most of the Bayer data is very dark. Here, tone mapping techniques with consistent outputs is achievable because the medium-exposed image may control the entire brightness of the image, resulting in an image that is identical to or close to an auto-exposed image. 
     The resulting fused image is provided to an inverse ISP pipeline operation  950 , which generates a fused YUV image that covers the full dynamic range.  FIG. 12  illustrates an example inverse ISP operation  950  of  FIG. 9  in accordance with this disclosure. The inverse ISP pipeline operation  950  is the inverse process of the ISP pipeline operation  930 . As shown in  FIG. 12 , a YUV-to-RGB conversion operation  951  converts the YUV domain image into an RGB domain image, such as 12-bit RGB data. An inverse Gamma operation  952 , an inverse CCM operation  953 , and an inverse DRC operation  954  apply inverses of the operations  934 ,  933 , and  932 , respectively. 
     A demosaic operation  960  generally operates to convert the original input image I 16  into RGB format (R o , G o , B o ), such as from a 16-bit Bayer format to a 12-bit RGB format. A gain map generation operation  970  generally operates to compute a gain map for each color plane (red, green, and blue). In some cases, this can be expressed as: 
                         g   ⁢   a   ⁢   i   ⁢     n   R       =       R   f       max   ⁡     (       R   0     ,   ɛ     )           ,       gain   G     =       G   f       max   ⁡     (       G   0     ,   ɛ     )           ,       g   ⁢   a   ⁢   i   ⁢     n   B       =     B     max   ⁡     (       B   0     ,   ɛ     )             ⁢     
     ⁢           ⁢     gain   =       0.25   *   g   ⁢   a   ⁢   i   ⁢     n   R       +       0   .   5     *   g   ⁢   a   ⁢   i   ⁢     n   G       +       0   .   2     ⁢   5   *   g   ⁢   a   ⁢   i   ⁢     n   B                   (   28   )               
An apply gain map operation  980  applies the generated gain maps to the original input Bayer image to yield a final tone-mapped Bayer image. In some embodiments, the gain maps may be half the size of the input Bayer image  210 , and the gain value applied to each pixel in the Bayer image may be an interpolated value that is based on the gain maps (although this need not be the case). Gain values may be generally low for brighter areas of the input image and generally high for darker areas of the input image.
 
       FIG. 13  illustrates an example output of an image fusion operation in accordance with this disclosure. More specifically,  FIG. 13  illustrates an example short exposed YUV image  1310 , a medium exposed YUV image  1320 , a long exposed YUV image  1330 , and the resulting fused YUV image  1340  that may be generated using the process  900 . As can be seen here, the resulting fused YUV image  1340  covers the full dynamic range of the images  1310 ,  1320 , and  1330 . 
     Note that the functions and other operations described above with reference to  FIGS. 9 through 13  can be implemented in an electronic device  101 ,  102 ,  104 , server  106 , or other device in any suitable manner. For example, in some embodiments, the operations described above can be implemented or supported using one or more software applications or other software instructions that are executed by at least one processor  120  of a device. In other embodiments, at least some of the operations described above can be implemented or supported using dedicated hardware components. In general, the operations described above can be performed using any suitable hardware or any suitable combination of hardware and software/firmware instructions. 
     Although  FIGS. 9 through 13  illustrate one example of a YUV-based process  900  for dynamic range compression and related details, various changes may be made to  FIGS. 9 through 13 . For example, each operation in  FIGS. 9, 11, and 12  may occur any number of times as needed or desired in order to process input images and generate output images. Also, the images shown in  FIGS. 10 and 13  are for illustration only and can vary widely based on the image data being processed. 
       FIG. 14  illustrates an example method  1400  for dynamic range compression in accordance with this disclosure. For ease of explanation, the method  1400  is described as being implemented using the electronic device  101  shown in  FIG. 1 . However, the method  1400  could be implemented in any other suitable electronic device and in any suitable system, such as by the server  106 . 
     As shown in  FIG. 14 , in step  1410 , the electronic device  101  obtains an HDR input Bayer image. This may include, for example, the processor  120  of the electronic device  101  generating an HDR input Bayer image based on image frames captured using one or more sensors  180  of the electronic device. In step  1420 , the electronic device  101  generates a plurality of synthesized images at different exposure levels based on the input Bayer image. This may include, for example, the processor  120  of the electronic device  101  generating two or more synthesized images at different exposure levels, such as short, medium, and long images. In step  1430 , the electronic device  101  fuses the synthesized images to generate a fused or blended image. This may include, for example, the processor  120  of the electronic device  101  performing pyramid blending of the synthesized images. 
     In step  1440 , the electronic device  101  generates a gain map based on the fused image. This may include, for example, the processor  120  of the electronic device  101  transforming the fused or blended image into a first RGB image, converting the input Bayer image into a second RGB image using a demosaic function to compute red, green, and blue plane gain maps, and generating the gain map based on the first RGB image and the red, green, and blue plane gain maps. In step  1450 , the electronic device  101  applies gains to the input Bayer image. This may include, for example, the processor  120  of the electronic device  101  applying the gains as identified in the gain map to the input Bayer image. 
     Although  FIG. 14  illustrates one example of a method  1500  for dynamic range compression, various changes may be made to  FIG. 14 . For example, while shown as a series of steps, various steps in  FIG. 14  may overlap, occur in parallel, occur in a different order, or occur any number of times. 
     Although this disclosure has been described with reference to various example embodiments, various changes and modifications may be suggested to one skilled in the art. It is intended that this disclosure encompass such changes and modifications as fall within the scope of the appended claims.