WO2024124395A1 - Filtrage temporel pour correction de distorsions de mouvement - Google Patents

Filtrage temporel pour correction de distorsions de mouvement Download PDF

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Publication number
WO2024124395A1
WO2024124395A1 PCT/CN2022/138625 CN2022138625W WO2024124395A1 WO 2024124395 A1 WO2024124395 A1 WO 2024124395A1 CN 2022138625 W CN2022138625 W CN 2022138625W WO 2024124395 A1 WO2024124395 A1 WO 2024124395A1
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Prior art keywords
image frame
image
determining
motion
frame
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PCT/CN2022/138625
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English (en)
Inventor
Kai Liu
Chung-Yan Chih
Zhongshan WANG
Wen-Chun Feng
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Qualcomm Incorporated
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Priority to PCT/CN2022/138625 priority Critical patent/WO2024124395A1/fr
Publication of WO2024124395A1 publication Critical patent/WO2024124395A1/fr

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  • aspects of the present disclosure relate generally to image processing, and more particularly, to processing images to correct for motion artifacts and other errors. Some features may enable and provide improved image processing, including processing images using temporal filtration.
  • Image capture devices are devices that can capture one or more digital images, whether still images for photos or sequences of images for videos. Capture devices can be incorporated into a wide variety of devices.
  • image capture devices may comprise stand-alone digital cameras or digital video camcorders, camera-equipped wireless communication device handsets, such as mobile telephones, cellular or satellite radio telephones, personal digital assistants (PDAs) , panels or tablets, gaming devices, computing devices such as webcams, video surveillance cameras, or other devices with digital imaging or video capabilities.
  • PDAs personal digital assistants
  • gaming devices such as webcams, video surveillance cameras, or other devices with digital imaging or video capabilities.
  • Movement of subjects while capturing image frames can create various distortions within the image frames. For example, movement of one or more objects within an image frame may cause the objects to blur and/or blend together or may leave motion artifacts within the captured image frame.
  • Image frames may be received to detect and correct motion distortion and motion artifacts within the image frames.
  • the image frames may be analyzed (e.g., sequentially analyzed) to correct these errors.
  • An earlier, first image frame may be compared to a later, second image frame to generate a motion map, which may indicate motion within the first image frame relative to the second image frame.
  • the motion map may indicate local movement of objects depicted within the second image frame in the first image frame.
  • the motion map may reflect global motion of the device used to capture the image frames.
  • a gain map may be computed for the first image frame.
  • the gain map may be computed to correct or balance luminance values within the first image frame.
  • One or more parameters for a temporal filtering process may be computed based on the motion map and/or the gain map. For example, a dynamic motion blending strength may be determined based on the motion map and/or a dynamic spatial blending strength may be determined based on the gain map.
  • a temporal filtering process may be applied to the first image frame according to the one or more parameters to generate a corrected image frame for the first image frame.
  • the temporal filtering process may blend multiple versions of the first image frame, including an original version of the first image frame, an aligned version of the first image frame, and a denoised version of the first image frame.
  • the dynamic motion blending strength may adjust how strongly the aligned version of the first image frame is blended to generate the corrected image frame.
  • the dynamic spatial blending strength may adjust how strongly the denoised version of the first image frame is blended to generate the corrected image frame.
  • the temporal filtering process may be applied on a per-pixel basis to generate the corrected image frame.
  • the corrected image frame may then be added to an output file. This process may be repeated to process multiple image frames. For example, this process may be sequentially repeated across all of the received image frames (e.g., in the order in which the image frames are captured and/or received) .
  • difference thresholds inform blending strengths.
  • frames can be aligned based on local gains and can be adjusted to compensate for motion and/or luminance variations between frames to be blended. These adjustments may lead to alignment distortions and/or imbalanced noise levels due to local gain compensations.
  • the present disclosure describes, in part, techniques that use motion magnitude and a gain map to adjust blending strengths within image frames, such as on a pixelwise basis. More specifically, motion magnitude may be used to adjust a temporal filtering threshold and blending strength since motion warped regions may have closer gaps between noise and true signal.
  • the temporal filtering threshold may be decreased in areas with higher motion to maintain the benefit of small local motion warping while avoiding artifacts introduced by blending pixels with large amounts of motion.
  • the gain map may be used to adjust temporal filtering strength since both noise and signal are enlarged by local gain.
  • a method for image processing includes receiving image data including a first image frame; determining at least one of a gain map for the first image frame or a motion map for the first image frame; determining at least one parameter for a temporal filtering process based on the at least one of the gain map and the motion map; and determining a corrected image frame by temporally filtering the first image frame based on the at least one parameter.
  • an apparatus includes at least one processor and a memory coupled to the at least one processor.
  • the at least one processor is configured to receive image data including a first image frame; determine at least one of a gain map for the first image frame or a motion map for the first image frame; determine at least one parameter for a temporal filtering process based on the at least one of the gain map and the motion map; and determine a corrected image frame by temporally filtering the first image frame based on the at least one parameter.
  • a method for image processing includes receiving image data including a first image frame; determining a motion map for the first image frame that identifies movement within the first image frame; determining an aligned frame based on the motion map and the first image frame, wherein the aligned frame corrects for movement distortion within the first image frame; determining a dynamic motion blending strength based on the motion map; and determining a corrected image frame by temporally filtering the first image frame and the aligned frame based on the dynamic motion blending strength.
  • an apparatus includes at least one processor and a memory coupled to the at least one processor.
  • the at least one processor is configured to execute the processor-readable code to cause the at least one processor to perform operations including: receiving image data including a first image frame; determining a motion map for the first image frame that identifies movement within the first image frame; determining an aligned frame based on the motion map and the first image frame, wherein the aligned frame corrects for movement distortion within the first image frame; determining a dynamic motion blending strength based on the motion map; and determining a corrected image frame by temporally filtering the first image frame and the aligned frame based on the dynamic motion blending strength.
  • Image capture devices devices that can capture one or more digital images, whether still image photos or sequences of images for videos, can be incorporated into a wide variety of devices.
  • image capture devices may comprise stand-alone digital cameras or digital video camcorders, camera-equipped wireless communication device handsets, such as mobile telephones, cellular or satellite radio telephones, personal digital assistants (PDAs) , panels or tablets, gaming devices, computing devices such as webcams, video surveillance cameras, or other devices with digital imaging or video capabilities.
  • PDAs personal digital assistants
  • the image processing techniques described herein may involve digital cameras having image sensors and processing circuitry (e.g., application specific integrated circuits (ASICs) , digital signal processors (DSP) , graphics processing unit (GPU) , or central processing units (CPU) ) .
  • An image signal processor (ISP) may include one or more of these processing circuits and may be configured to perform operations to obtain the image data for processing according to the image processing techniques described herein and/or involved in the image processing techniques described herein.
  • the ISP may be configured to control the capture of image frames from one or more image sensors and determine one or more image frames from the one or more image sensors to generate a view of a scene in an output image frame.
  • the output image frame may be part of a sequence of image frames forming a video sequence.
  • the video sequence may include other image frames received from the image sensor or other images sensors.
  • the image signal processor may receive an instruction to capture a sequence of image frames in response to the loading of software, such as a camera application, to produce a preview display from the image capture device.
  • the image signal processor may be configured to produce a single flow of output image frames, based on images frames received from one or more image sensors.
  • the single flow of output image frames may include raw image data from an image sensor, binned image data from an image sensor, or corrected image data processed by one or more algorithms within the image signal processor.
  • an image frame obtained from an image sensor which may have performed some processing on the data before output to the image signal processor, may be processed in the image signal processor by processing the image frame through an image post-processing engine (IPE) and/or other image processing circuitry for performing one or more of tone mapping, portrait lighting, contrast enhancement, gamma correction, etc.
  • IPE image post-processing engine
  • the output image frame from the ISP may be stored in memory and retrieved by an application processor executing the camera application, which may perform further processing on the output image frame to adjust an appearance of the output image frame and reproduce the output image frame on a display for view by the user.
  • the output image frame may be displayed on a device display as a single still image and/or as part of a video sequence, saved to a storage device as a picture or a video sequence, transmitted over a network, and/or printed to an output medium.
  • the image signal processor may be configured to obtain input frames of image data (e.g., pixel values) from the one or more image sensors, and in turn, produce corresponding output image frames (e.g., preview display frames, still-image captures, frames for video, frames for object tracking, etc. ) .
  • the image signal processor may output image frames to various output devices and/or camera modules for further processing, such as for 3A parameter synchronization (e.g., automatic focus (AF) , automatic white balance (AWB) , and automatic exposure control (AEC) ) , producing a video file via the output frames, configuring frames for display, configuring frames for storage, transmitting the frames through a network connection, etc.
  • 3A parameter synchronization e.g., automatic focus (AF) , automatic white balance (AWB) , and automatic exposure control (AEC)
  • AF automatic focus
  • ABB automatic white balance
  • AEC automatic exposure control
  • the image signal processor may obtain incoming frames from one or more image sensors and produce and output a flow of output frames to various output destinations.
  • the output image frame may be produced by combining aspects of the image correction of this disclosure with other computational photography techniques such as high dynamic range (HDR) photography or multi-frame noise reduction (MFNR) .
  • HDR photography a first image frame and a second image frame are captured using different exposure times, different apertures, different lenses, and/or other characteristics that may result in improved dynamic range of a fused image when the two image frames are combined.
  • the method may be performed for MFNR photography in which the first image frame and a second image frame are captured using the same or different exposure times and fused to generate a corrected first image frame with reduced noise compared to the captured first image frame.
  • a device may include an image signal processor or a processor (e.g., an application processor) including specific functionality for camera controls and/or processing, such as enabling or disabling the binning module or otherwise controlling aspects of the image correction.
  • image signal processor or a processor e.g., an application processor
  • the methods and techniques described herein may be entirely performed by the image signal processor or a processor, or various operations may be split between the image signal processor and a processor, and in some aspects split across additional processors.
  • the device may include one, two, or more image sensors, such as a first image sensor.
  • the image sensors may be differently configured.
  • the first image sensor may have a larger field of view (FOV) than the second image sensor, or the first image sensor may have different sensitivity or different dynamic range than the second image sensor.
  • the first image sensor may be a wide-angle image sensor, and the second image sensor may be a tele image sensor.
  • the first sensor is configured to obtain an image through a first lens with a first optical axis and the second sensor is configured to obtain an image through a second lens with a second optical axis different from the first optical axis.
  • the first lens may have a first magnification
  • the second lens may have a second magnification different from the first magnification.
  • Any of these or other configurations may be part of a lens cluster on a mobile device, such as where multiple image sensors and associated lenses are located in offset locations on a frontside or a backside of the mobile device. Additional image sensors may be included with larger, smaller, or same field of views.
  • the image processing techniques described herein may be applied to image frames captured from any of the image sensors in a multi-sensor device.
  • a device configured for image processing and/or image capture.
  • the apparatus includes means for capturing image frames.
  • the apparatus further includes one or more means for capturing data representative of a scene, such as image sensors (including charge-coupled devices (CCDs) , Bayer-filter sensors, infrared (IR) detectors, ultraviolet (UV) detectors, complimentary metal-oxide-semiconductor (CMOS) sensors) and time of flight detectors.
  • the apparatus may further include one or more means for accumulating and/or focusing light rays into the one or more image sensors (including simple lenses, compound lenses, spherical lenses, and non-spherical lenses) . These components may be controlled to capture the first and/or second image frames input to the image processing techniques described herein.
  • the method may be embedded in a computer-readable medium as computer program code comprising instructions that cause a processor to perform the steps of the method.
  • the processor may be part of a mobile device including a first network adaptor configured to transmit data, such as images or videos in a recording or as streaming data, over a first network connection of a plurality of network connections; and a processor coupled to the first network adaptor and the memory.
  • the processor may cause the transmission of output image frames described herein over a wireless communications network such as a 5G NR communication network.
  • aspects and implementations are described in this application by illustration to some examples, those skilled in the art will understand that additional implementations and use cases may come about in many different arrangements and scenarios. Innovations described herein may be implemented across many differing platform types, devices, systems, shapes, sizes, and packaging arrangements. For example, aspects and/or uses may come about via integrated chip implementations and other non-module-component based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, artificial intelligence (AI) -enabled devices, etc. ) . While some examples may or may not be specifically directed to use cases or applications, a wide assortment of applicability of described innovations may occur.
  • non-module-component based devices e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/purchasing devices, medical devices, artificial intelligence (AI) -enabled devices, etc.
  • AI artificial intelligence
  • Implementations may range in spectrum from chip-level or modular components to non-modular, non-chip-level implementations and further to aggregate, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more aspects of the described innovations.
  • devices incorporating described aspects and features may also necessarily include additional components and features for implementation and practice of claimed and described aspects.
  • transmission and reception of wireless signals necessarily includes a number of components for analog and digital purposes (e.g., hardware components including antenna, radio frequency (RF) -chains, power amplifiers, modulators, buffer, processor (s) , interleaver, adders/summers, etc. ) .
  • RF radio frequency
  • s interleaver
  • adders/summers etc.
  • Figure 1 shows a block diagram of an example device for performing image capture from one or more image sensors.
  • Figure 2 is a block diagram illustrating an example data flow path for image data processing in an image capture device according to one or more embodiments of the disclosure.
  • Figure 3 is a block diagram of an example implementation of an image signal processor according to an exemplary embodiment of the disclosure.
  • Figure 4 shows a flow chart of an example method for processing image frames to correct for motion distortion according to some embodiments of the disclosure.
  • Figure 5 is a block diagram illustrating an example processor configuration for image data processing in an image capture device according to one or more embodiments of the disclosure.
  • a gain map and/or a motion map may be computed for a received image frame.
  • One or more parameters may be computed based on the gain map and/or the motion map.
  • the one or more parameters may then be used to dynamically alter how a temporal filtering process is applied to the received image frame.
  • the one or more parameters may alter how strongly a motion aligned image frame is blended into a corrected image frame.
  • the one or more parameters may alter how strongly a denoised image frame is blended into a corrected image frame.
  • the present disclosure provides techniques for addressing the shortcomings of previous temporal filtering techniques.
  • the resulting images may have a better image quality as a result of the improved temporal filtering process applied to generate the corrected image frame.
  • the improved temporal filtering techniques may be able to incorporate the improved image detail enabled by the motion aligned image frames without also incorporating the motion alignment artifacts in other areas of the motion aligned frames.
  • the improved temporal filtering techniques may be able to incorporate the improved luminance values from local gain processing while also correcting for the higher noise values in high gain areas that are more prone to spatial noise. These techniques may accordingly improve computing devices’ ability to accurately capture images and/or video.
  • An example device for capturing image frames using one or more image sensors may include a configuration of one, two, three, four, or more cameras on a backside (e.g., a side opposite a primary user display) and/or a front side (e.g., a same side as a primary user display) of the device.
  • the devices may include one or more image signal processors (ISPs) , Computer Vision Processors (CVPs) (e.g., AI engines) , or other suitable circuitry for processing images captured by the image sensors.
  • the one or more image signal processors (ISP) may store output image frames in a memory and/or otherwise provide the output image frames to processing circuitry (such as through a bus) .
  • the processing circuitry may perform further processing, such as for encoding, storage, transmission, or other manipulation of the output image frames.
  • image sensor may refer to the image sensor itself and any certain other components coupled to the image sensor used to generate an image frame for processing by the image signal processor or other logic circuitry or storage in memory, whether a short-term buffer or longer-term non-volatile memory.
  • an image sensor may include other components of a camera, including a shutter, buffer, or other readout circuitry for accessing individual pixels of an image sensor.
  • the image sensor may further refer to an analog front end or other circuitry for converting analog signals to digital representations for the image frame that are provided to digital circuitry coupled to the image sensor.
  • a single block may be described as performing a function or functions.
  • the function or functions performed by that block may be performed in a single component or across multiple components, and/or may be performed using hardware, software, or a combination of hardware and software.
  • various illustrative components, blocks, modules, circuits, and steps are described below generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
  • the example devices may include components other than those shown, including well-known components such as a processor, memory, and the like.
  • aspects of the present disclosure are applicable to any electronic device including, coupled to, or otherwise processing data from one, two, or more image sensors capable of capturing image frames (or “frames” ) .
  • the terms “output image frame” and “corrected image frame” may refer to image frames that have been processed by any of the discussed techniques.
  • aspects of the present disclosure may be implemented in devices having or coupled to image sensors of the same or different capabilities and characteristics (such as resolution, shutter speed, sensor type, and so on) .
  • aspects of the present disclosure may be implemented in devices for processing image frames, whether or not the device includes or is coupled to the image sensors, such as processing devices that may retrieve stored images for processing, including processing devices present in a cloud computing system.
  • a device may be any electronic device with one or more parts that may implement at least some portions of the disclosure. While the description and examples herein use the term “device” to describe various aspects of the disclosure, the term “device” is not limited to a specific configuration, type, or number of objects.
  • an apparatus may include a device or a portion of the device for performing the described operations.
  • Certain components in a device or apparatus described as “means for accessing, ” “means for receiving, ” “means for sending, ” “means for using, ” “means for selecting, ” “means for determining, ” “means for normalizing, ” “means for multiplying, ” or other similarly-named terms referring to one or more operations on data, such as image data, may refer to processing circuitry (e.g., application specific integrated circuits (ASICs) , digital signal processors (DSP) , graphics processing unit (GPU) , central processing unit (CPU) ) configured to perform the recited function through hardware, software, or a combination of hardware configured by software.
  • ASICs application specific integrated circuits
  • DSP digital signal processors
  • GPU graphics processing unit
  • CPU central processing unit
  • Figure 1 shows a block diagram of an example device 100 for performing image capture from one or more image sensors.
  • the device 100 may include, or otherwise be coupled to, an image signal processor 112 for processing image frames from one or more image sensors, such as a first image sensor 101, a second image sensor 102, and a depth sensor 140.
  • the device 100 also includes or is coupled to a processor 104 and a memory 106 storing instructions 108.
  • the device 100 may also include or be coupled to a display 114 and input/output (I/O) components 116. I/O components 116 may be used for interacting with a user, such as a touch screen interface and/or physical buttons.
  • I/O components 116 may be used for interacting with a user, such as a touch screen interface and/or physical buttons.
  • I/O components 116 may also include network interfaces for communicating with other devices, including a wide area network (WAN) adaptor 152, a local area network (LAN) adaptor 153, and/or a personal area network (PAN) adaptor 154.
  • WAN wide area network
  • LAN local area network
  • PAN personal area network
  • An example WAN adaptor is a 4G LTE or a 5G NR wireless network adaptor.
  • An example LAN adaptor 153 is an IEEE 802.11 WiFi wireless network adapter.
  • An example PAN adaptor 154 is a Bluetooth wireless network adaptor.
  • Each of the adaptors 152, 153, and/or 154 may be coupled to an antenna, including multiple antennas configured for primary and diversity reception and/or configured for receiving specific frequency bands.
  • the device 100 may further include or be coupled to a power supply 118 for the device 100, such as a battery or a component to couple the device 100 to an energy source.
  • the device 100 may also include or be coupled to additional features or components that are not shown in Figure 1.
  • a wireless interface which may include a number of transceivers and a baseband processor, may be coupled to or included in WAN adaptor 152 for a wireless communication device.
  • an analog front end (AFE) to convert analog image frame data to digital image frame data may be coupled between the image sensors 101 and 102 and the image signal processor 112.
  • AFE analog front end
  • the device may include or be coupled to a sensor hub 150 for interfacing with sensors to receive data regarding movement of the device 100, data regarding an environment around the device 100, and/or other non-camera sensor data.
  • a non-camera sensor is a gyroscope, a device configured for measuring rotation, orientation, and/or angular velocity to generate motion data.
  • Another example non-camera sensor is an accelerometer, a device configured for measuring acceleration, which may also be used to determine velocity and distance traveled by appropriately integrating the measured acceleration, and one or more of the acceleration, velocity, and/or distance may be included in generated motion data.
  • a gyroscope in an electronic image stabilization system (EIS) may be coupled to the sensor hub or coupled directly to the image signal processor 112.
  • a non-camera sensor may be a global positioning system (GPS) receiver.
  • GPS global positioning system
  • the image signal processor 112 may receive image data, such as used to form image frames.
  • a local bus connection couples the image signal processor 112 to image sensors 101 and 102 of a first camera 103 and second camera 105, respectively.
  • a wire interface couples the image signal processor 112 to an external image sensor.
  • a wireless interface couples the image signal processor 112 to the image sensor 101, 102.
  • the first camera 103 may include the first image sensor 101 and a corresponding first lens 131.
  • the second camera may include the second image sensor 102 and a corresponding second lens 132.
  • Each of the lenses 131 and 132 may be controlled by an associated autofocus (AF) algorithm 133 executing in the ISP 112, which adjust the lenses 131 and 132 to focus on a particular focal plane at a certain scene depth from the image sensors 101 and 102.
  • the AF algorithm 133 may be assisted by depth sensor 140.
  • the first image sensor 101 and the second image sensor 102 are configured to capture one or more image frames.
  • Lenses 131 and 132 focus light at the image sensors 101 and 102, respectively, through one or more apertures for receiving light, one or more shutters for blocking light when outside an exposure window, one or more color filter arrays (CFAs) for filtering light outside of specific frequency ranges, one or more analog front ends for converting analog measurements to digital information, and/or other suitable components for imaging.
  • the first lens 131 and second lens 132 may have different field of views to capture different representations of a scene.
  • the first lens 131 may be an ultra-wide (UW) lens and the second lens 132 may be a wide (W) lens.
  • the multiple image sensors may include a combination of ultra-wide (high field-of-view (FOV) ) , wide, tele, and ultra-tele (low FOV) sensors.
  • each image sensor may be configured through hardware configuration and/or software settings to obtain different, but overlapping, field of views.
  • the image sensors are configured with different lenses with different magnification ratios that result in different fields of view.
  • the sensors may be configured such that a UW sensor has a larger FOV than a W sensor, which has a larger FOV than a T sensor, which has a larger FOV than a UT sensor.
  • a sensor configured for wide FOV may capture fields of view in the range of 64-84 degrees
  • a sensor configured for ultra-side FOV may capture fields of view in the range of 100-140 degrees
  • a sensor configured for tele FOV may capture fields of view in the range of 10-30 degrees
  • a sensor configured for ultra-tele FOV may capture fields of view in the range of 1-8 degrees.
  • the camera 103 may be a variable aperture (VA) camera in which the aperture can be controlled to a particular size.
  • VA variable aperture
  • Example aperture sizes are f/2.0, f/2.8, f/3.2, f/8.0, etc. Larger aperture values correspond to smaller aperture sizes, and smaller aperture values correspond to larger aperture sizes.
  • the camera 103 may have different characteristics based on the current aperture size, such as a different depth of focus (DOF) at different aperture sizes.
  • DOE depth of focus
  • the image signal processor 112 processes image frames captured by the image sensors 101 and 102. While Figure 1 illustrates the device 100 as including two image sensors 101 and 102 coupled to the image signal processor 112, any number (e.g., one, two, three, four, five, six, etc. ) of image sensors may be coupled to the image signal processor 112. In some aspects, depth sensors such as depth sensor 140 may be coupled to the image signal processor 112, and output from the depth sensors are processed in a similar manner to that of image sensors 101 and 102.
  • Example depth sensors include active sensors, including one or more of indirect Time of Flight (iToF) , direct Time of Flight (dToF) , light detection and ranging (Lidar) , mmWave, radio detection and ranging (Radar) , and/or hybrid depth sensors, such as structured light.
  • iToF indirect Time of Flight
  • dToF direct Time of Flight
  • Lidar light detection and ranging
  • mmWave mmWave
  • radio detection and ranging Radar
  • hybrid depth sensors such as structured light.
  • similar information regarding depth of objects or a depth map may be generated in a passive manner from the disparity between two image sensors (e.g., using depth-from-disparity or depth-from-stereo) , phase detection auto-focus (PDAF) sensors, or the like.
  • PDAF phase detection auto-focus
  • any number of additional image sensors or image signal processors may exist for the device 100.
  • the image signal processor 112 may execute instructions from a memory, such as instructions 108 from the memory 106, instructions stored in a separate memory coupled to or included in the image signal processor 112, or instructions provided by the processor 104.
  • the image signal processor 112 may include specific hardware (such as one or more integrated circuits (ICs) ) configured to perform one or more operations described in the present disclosure.
  • the image signal processor 112 may include one or more image front ends (IFEs) 135, one or more image post-processing engines 136 (IPEs) , one or more auto exposure compensation (AEC) 134 engines, and/or one or more engines for video analytics (EVAs) .
  • IFEs image front ends
  • IPEs image post-processing engines
  • AEC auto exposure compensation
  • EVAs video analytics
  • the AF 133, AEC 134, IFE 135, IPE 136, and EVA 137 may each include application-specific circuitry, be embodied as software code executed by the ISP 112, and/or a combination of hardware and software code executing on the ISP 112.
  • the memory 106 may include a non-transient or non-transitory computer readable medium storing computer-executable instructions 108 to perform all or a portion of one or more operations described in this disclosure.
  • the instructions 108 include a camera application (or other suitable application) to be executed by the device 100 for generating images or videos.
  • the instructions 108 may also include other applications or programs executed by the device 100, such as an operating system and specific applications other than for image or video generation. Execution of the camera application, such as by the processor 104, may cause the device 100 to generate images using the image sensors 101 and 102 and the image signal processor 112.
  • the memory 106 may also be accessed by the image signal processor 112 to store processed frames or may be accessed by the processor 104 to obtain the processed frames.
  • the device 100 does not include the memory 106.
  • the device 100 may be a circuit including the image signal processor 112, and the memory may be outside the device 100.
  • the device 100 may be coupled to an external memory and configured to access the memory for writing output frames for display or long-term storage.
  • the device 100 is a system-on-chip (SoC) that incorporates the image signal processor 112, the processor 104, the sensor hub 150, the memory 106, and input/output components 116 into a single package.
  • SoC system-on-chip
  • the image signal processor 112 or the processor 104 executes instructions to perform various operations described herein, including motion distortion correction and noise balancing operations. For example, execution of the instructions can instruct the image signal processor 112 to begin or end capturing an image frame or a sequence of image frames, in which the capture includes motion distortion as described in embodiments herein.
  • the processor 104 may include one or more general-purpose processor cores 104A capable of executing scripts or instructions of one or more software programs, such as instructions 108 stored within the memory 106.
  • the processor 104 may include one or more application processors configured to execute the camera application (or other suitable application for generating images or video) stored in the memory 106.
  • the processor 104 may be configured to instruct the image signal processor 112 to perform one or more operations with reference to the image sensors 101 or 102.
  • a camera application executing on processor 104 may receive a user command to begin a video preview display upon which a video comprising a sequence of image frames is captured and processed from one or more image sensors 101 or 102 through the image signal processor 112.
  • Image processing to generate “output” or “corrected” image frames may be applied to one or more image frames in the sequence.
  • Execution of instructions 108 outside of the camera application by the processor 104 may also cause the device 100 to perform any number of functions or operations.
  • the processor 104 may include ICs or other hardware (e.g., an artificial intelligence (AI) engine 124 or other co-processor) to offload certain tasks from the cores 104A.
  • AI artificial intelligence
  • the AI engine 124 may be used to offload tasks related to, for example, face detection and/or object recognition.
  • the device 100 does not include the processor 104, such as when all of the described functionality is configured in the image signal processor 112.
  • the display 114 may include one or more suitable displays or screens allowing for user interaction and/or to present items to the user, such as a preview of the image frames being captured by the image sensors 101 and 102.
  • the display 114 is a touch-sensitive display.
  • the I/O components 116 may be or include any suitable mechanism, interface, or device to receive input (such as commands) from the user and to provide output to the user through the display 114.
  • the I/O components 116 may include (but are not limited to) a graphical user interface (GUI) , a keyboard, a mouse, a microphone, speakers, a squeezable bezel, one or more buttons (such as a power button) , a slider, a switch, and so on.
  • GUI graphical user interface
  • APU application processor unit
  • SoC system on chip
  • the exemplary image capture device of Figure 1 may be operated to obtain improved images utilizing improved motion distortion correction techniques, such as improved temporal filtering techniques.
  • improved motion distortion correction techniques such as improved temporal filtering techniques.
  • FIG. 2 is a block diagram illustrating an example data flow path for image data processing in an image capture device according to one or more embodiments of the disclosure.
  • a processor 104 of system 200 may communicate with image signal processor (ISP) 112 through a bi-directional bus and/or separate control and data lines.
  • the processor 104 may control camera 103 through camera control 210, such as for configuring the camera 103 through a driver executing on the processor 104.
  • the camera control 210 may be managed by a camera application 204 executing on the processor 104, which provides settings accessible to a user such that a user can specify individual camera settings or select a profile with corresponding camera settings.
  • the camera control 210 communicates with the camera 103 to configure the camera 103 in accordance with commands received from the camera application 204.
  • the camera application 204 may be, for example, a photography application, a document scanning application, a messaging application, or other application that processes image data acquired from camera 103.
  • the camera configuration may parameters that specify, for example, a frame rate, an image resolution, a readout duration, an exposure level, an aspect ratio, an aperture size, etc.
  • the camera 103 may obtain image data based on the camera configuration.
  • the processor 104 may execute a camera application 204 to instruct camera 103, through camera control 210, to set a first camera configuration for the camera 103, to obtain first image data from the camera 103 operating in the first camera configuration, to instruct camera 103 to set a second camera configuration for the camera 103, and to obtain second image data from the camera 103 operating in the second camera configuration.
  • the processor 104 may execute a camera application 204 to instruct camera 103 to configure to a first aperture size, obtain first image data from the camera 103, instruct camera 103 to configure to a second aperture size, and obtain second image data from the camera 103.
  • the reconfiguration of the aperture and obtaining of the first and second image data may occur with little or no change in the scene captured at the first aperture size and the second aperture size.
  • Example aperture sizes are f/2.0, f/2.8, f/3.2, f/8.0, etc. Larger aperture values correspond to smaller aperture sizes, and smaller aperture values correspond to larger aperture sizes. That is, f/2.0 is a larger aperture size than f/8.0.
  • the image data received from camera 103 may be processed in one or more blocks of the ISP 112 to form image frames 230 that are stored in memory 106 and/or provided to the processor 104.
  • the ISP 112 may process received image frames using temporal filtering to correct for movement of objects within the image frames.
  • the processor 104 may further process the image data to apply effects to the image frames 230. Effects may include Bokeh, lighting, color casting, and/or high dynamic range (HDR) merging.
  • functionality may be embedded in a different component, such as the ISP 112, a DSP, an ASIC, or other custom logic circuit for performing the additional image processing.
  • FIG. 3 is a block diagram of a system 300 according to an exemplary embodiment of the present disclosure.
  • the system 300 includes image frames 302, 304, the ISP 112, and output image frames 332.
  • the ISP 112 may be configured to receive one or more image frames 302, 304 and to generate a corrected image frame 320 based on the image frames 302, 304. Specifically, the ISP 112 may be configured to generate the corrected image frame 320 to correct for motion distortion and/or motion artifacts located within a first image frame 302.
  • the image frames 302, 304 may be received as image data from an image sensor, such as the image sensors 101, 102.
  • the ISP 112 may be configured to receive and sequentially process image frames (e.g., as part of an image processing pipeline and/or a video processing pipeline) .
  • the ISP 112 may initially receive the image frame 304 (e.g., after being captured by the image sensor 101, 102) and may subsequently receive the image frame 302 (e.g., after being subsequently captured by the image sensor 101, 102) .
  • the image frame 302 may be considered a current image frame, or target image frame of an image processing or video processing pipeline, and the image frame 304 may be considered a reference frame of the image processing pipeline.
  • the ISP 112 may receive a plurality of image frames including the image frames 302, 304. For example, a plurality of image frames may be previously stored and may be retrieved by the ISP 112 for further processing.
  • the ISP may be configured to apply a temporal filtering process 314 to the image frame 302 to generate a corrected image frame 320.
  • the temporal filtering process 314 may blend multiple versions of the image frame 302 to generate the corrected image frame 320 that corrects for motion distortion within the image frame 302.
  • the temporal filtering process 314 may be configured to blend the image frame 302, a motion aligned frame 310, and a denoised frame 312 to generate the corrected image frame.
  • the denoised frame 312 may be generated to reduce noise (e.g., spatial noise, image noise, graininess) within the image frame 302. Noise within the image may be reduced according to one or more spatial denoising processes, such as MFNR.
  • the motion aligned frame 310 may be generated to remove or reduce movement within the image frame 302.
  • the motion aligned frame 310 may be generated to align the image frame 302 with a previously-captured image frame (e.g., the image frame 304) .
  • the motion aligned frame 310 may be configured by applying a motion alignment process to the image frame. For example, a blockwise process may be used to find motion vectors between corresponding blocks (such as 8x8 pixel blocks) of the image frames 302, 304.
  • the corresponding blocks may be identified by a semi-global matching (SGM) process to identify the most similar blocks in the image frames 302, 304. Pixels within the image frame 302 may then be warped based on the motion vectors to form the motion aligned frame 310.
  • SGM semi-global matching
  • the motion alignment process may be applied according to a motion map 306.
  • the ISP may determine the motion map 306 may be generated to reflect movement within the image frame 302 relative to a previous image frame 304.
  • the motion map 306 may be generated by comparing the first image frame to the second image frame to generate the motion map 306.
  • the motion map 306 may be computed based on differences between the second image frame 304 and the first image frame 302.
  • the motion map 306 may be computed based on sensor data (e.g., motion sensor data) from a device 100 (e.g., an image capture device) that captured the first image frame 302 and the second image frame 304.
  • the ISP 112 may be configured to compute one or more global motion estimates reflecting movement of the image capture device and local motion estimates reflecting movement of one or more objects depicted within the image frames 302, 304.
  • the global motion estimates may be calculated based on sensor data, such as gyroscope or accelerometer data indicating movement of the image capture device.
  • the global motion estimates may include one or both of a magnitude and direction of movement for the image capture device.
  • Local motion estimates may be captured by comparing the first and second image frames 302, 304.
  • the local motion estimates may be determined by comparing the locations of one or more objects within each of the first image frame 302 and the second image frame 304.
  • the local motion estimates may be computed as differences between the image frames 302, 304.
  • local motion estimates may be calculated based on texture processing using Harris corner detection and related techniques. Local motion estimates and/or global motion estimates may then be combined to generate the motion map 306.
  • the motion map 306 may be computed by comparing the first image frame 302 to the second image frame 304 to generate an estimate of motion vectors between the image frames 302, 304.
  • the motion vectors and sensor data e.g., motion sensor data
  • the ISP 112 may then perform a matching process based on the alignment and the image frames 302, 304 to generate the motion map 306.
  • the matching process may be performed as a semi-global matching (SGM) process.
  • the contents of the motion map 306 may correspond to particular portions of the image frame 302.
  • each pixel of the image frame 302 may have a corresponding entry in the motion map 306.
  • each entry in the motion map 306 may correspond to multiple pixels (e.g., 4 pixels, 9 pixels, 16 pixels, or more) within the image frame 302.
  • the contents of the motion map 306 may indicate movement within corresponding portions of the image frame 302.
  • entries in the motion map 306 may indicate a magnitude of movement for an object or feature depicted within a corresponding portion of the image frame 302 (e.g., relative to the image frame 304) .
  • the magnitude of movement may be indicated as one or more of a number of pixels of movement, a distance of movement, a speed of movement, and the like.
  • motion may only be indicated within the motion map 306 if the motion exceeds a predetermined threshold.
  • a modified motion map may be computed as:
  • M MOD (n x , n y ) is the modified motion map value for pixel (n x , n y ) of the first image frame 302,
  • M (n x , n y ) is the original motion map value for pixel (n x , n y ) of the first image frame 302 (e.g., computed according to the above-described techniques) , and
  • MotionWeight ⁇ [0, 1] is a tunable parameter.
  • the ISP 112 may also be configured to generate a gain map 308 based on the image frame 302.
  • the gain map 308 may be generated based on luminance values within the image frame 302.
  • the gain map 308 may be generated to correct for low luminance values within the first.
  • the gain map 308 may include gain increases (e.g., increases in the brightness of the image frame 302) on a per-pixel basis within the image frame 302 to correct for portions of the image frame that have low luminance relative to other portions (e.g., nearby pixels, nearby regions) within the image frame 302.
  • the contents of the gain map 308 may correspond to particular portions of the image frame 302.
  • each pixel of the image frame 302 may have a corresponding entry in the gain map 308.
  • each entry in the gain map 308 may correspond to multiple pixels (e.g., 4 pixels, 9 pixels, 16 pixels, or more) within the image frame 302.
  • the contents of the gain map 308 may indicate an increase in luminance to be applied to corresponding portions of the image frame 302.
  • entries in the gain map 308 may indicate an increase in pixel value or magnitude for corresponding portions of the image frame 302.
  • the ISP may be configured to determine at least one parameter for the temporal filtering process 314 based on at least one of the gain map 308 and the motion map 306.
  • the temporal filtering process 314 includes a dynamic motion blending strength 316 and a dynamic spatial blending strength 318.
  • the dynamic motion blending strength 316 may be applied to the motion aligned frame 310 during the temporal filtering process 314. In particular, when blending the frames 302, 310, 312, higher values of the dynamic motion blending strength 316 may result in a stronger blending of the motion aligned frame 310.
  • the ISP 112 may compute the dynamic motion blending strength 316 based on the motion map 306.
  • the dynamic motion blending strength 316 may be computed on a per-pixel basis for each pixel within the image frame 302 based on corresponding values within the motion map 306. In certain implementations, the dynamic motion blending strength 316 increases for higher values within the motion map 306. As one specific example, the dynamic motion blending strength 316 may be computed as:
  • ⁇ Motion (n x , n y ) is the dynamic motion blending strength value for pixel (n x , n y ) of the first image frame 302,
  • ⁇ 1 is a predetermined static motion blending value (such as values from 0.5-1 in various implementations) .
  • M (n x , n y ) is the original motion map value for pixel (n x , n y ) of the first image frame 302 (M MOD (n x , n y ) may be used in certain implementations) .
  • the value of ⁇ 1 may be determined based on the motion map 306. For example, when a value of a corresponding portion of the motion map 306 is less than a predetermined threshold, the value of ⁇ 1 may be set to 1 to prevent applying motion blending in the corresponding region of the image frame 302, as motion correction is not required in that region.
  • the dynamic spatial blending strength 318 may be applied to the denoised frame 312 during the temporal filtering process 314. In particular, when blending the frames 302, 310, 312, higher values of the dynamic spatial blending strength 318 may result in a stronger blending of the denoised frame 312.
  • the ISP 112 may compute the dynamic spatial blending strength 318 based on the gain map 308. For example, the dynamic spatial blending strength 318 may be computed on a per-pixel basis for each pixel within the image frame 302 based on corresponding values within the gain map 308. In certain implementations, the dynamic spatial blending strength 318 increases for higher values within the gain map 308. As one specific example, the dynamic spatial blending strength 318 may be computed as:
  • Spatial (n x , n y ) is the dynamic spatial blending strength value for pixel (n x , n y ) of the first image frame 302,
  • ⁇ 2 is a predetermined static spatial blending value (such as values from 0- 1) , and
  • G (n x , n y ) is the gain map value for pixel (n x , n y ) of the first image frame 302.
  • the value of ⁇ 2 may be determined based on the motion map 306. For example, when a value of a corresponding portion of the motion map 306 exceeds a predetermined threshold, the value of ⁇ 2 may be set to 1 to apply spatial blending in the corresponding region of the image frame 302.
  • the ISP 112 may be configured to determine the corrected image frame 320 by temporally filtering the image frame 302 according to the temporal filtering process 314 and at least one of the dynamic motion blending strength 316 and the dynamic spatial blending strength 318.
  • the temporal filtering may be applied on a per-pixel basis.
  • the ISP 112 may iterate through each pixel of the image frame 302 and generate a corresponding corrected pixel within the corrected image frame 320 based on corresponding values of the motion aligned frame 310, the denoised frame 312, the dynamic motion blending strength, and the dynamic spatial blending strength.
  • output pixel values for the corrected image frame 320 may be computed as:
  • I Original (n x , n y ) is the pixel value for pixel (n x , n y ) of the first image frame 302,
  • I Aligned (n x , n y ) is the pixel value for pixel (n x , n y ) of the aligned frame 310,
  • Denoised (n x , n y ) is the pixel value for pixel (n x , n y ) of the denoised frame 312,
  • Spatial (n x , n y ) is the dynamic spatial blending strength value for pixel (n x , n y ) of the first image frame 302, and
  • ⁇ Motion (n x , n y ) is the dynamic motion blending strength value for pixel (n x , n y ) of the first image frame 302.
  • the corrected image frame 320 may be complete.
  • the ISP 112 (or another component of the device 100) may add the corrected image frame 320 to the output image frames 332 (e.g., output image frames for a video and/or composite image) .
  • the corrected image frame 320 may also serve as a reference image frame for correcting future image frames.
  • the second image frame 304 may be an image frame that was previously generated by the ISP 112 according to the temporal filtering process 314.
  • the dynamic motion blending strength 316 and/or the dynamic spatial blending strength 318 may not be separately computed for the image frame 302.
  • a predetermined spatial blending strength e.g., the default ⁇ 2 value
  • a predetermined motion blending strength e.g., the default ⁇ 1 value
  • the system 200 of Figure 2 and/or the system 300 of Figure 3 may be configured to perform the operations described with reference to Figure 4 to determine output image frames 230, 330.
  • Figure 3 shows a flow chart of an example method for processing image data to correct for motion distortion according to some embodiments of the disclosure.
  • the capturing in Figure 3 may obtain an improved digital representation of a scene, which results in a photograph or video with higher image quality (IQ) .
  • IQ image quality
  • image data is received that includes a first image frame.
  • the ISP 112 may receive image data including a first image frame 302.
  • the image data may be received from an image sensor 101, 102, such as while the image sensor is configured with the camera configuration.
  • the image data may be received at ISP 112, processed through an image front end (IFE) and/or an image post-processing engine (IPE) of the ISP 112, and stored in memory.
  • the capture of image data may be initiated by a camera application executing on the processor 104, which causes camera control 210 to activate capture of image data by the camera 103, and cause the image data to be supplied to a processor, such as processor 104 or ISP 112.
  • the ISP 112 may determine at least one of (i) a gain map 308 for the first image frame 302 and/or (ii) a motion map 306 for the first image frame 302.
  • a gain map 308 may be determined based on luminance values within the first image frame 302.
  • a motion map 306 may be determined by comparing the first image frame 302 to a second image frame 304 from the image data (e.g., a previously-captured image frame) .
  • At block 406 at least one parameter is determined for a temporal filtering process.
  • the ISP 112 may determine at least one parameter for a temporal filtering process 314 based on the at least one of the gain map 308 and the motion map 306.
  • the at least one parameter may include a dynamic motion blending strength 316 generated based on the motion map 306 and/or a dynamic spatial blending strength 318 generated based on the gain map 308.
  • at least one parameter may be generated on a per-pixel basis for the image frame 302.
  • a corrected image frame is determined by temporally filtering the first image frame based on the at least one parameter.
  • the ISP 112 may determine a corrected image frame 320 by applying a temporal filtering process 314 to the first image frame 302 according to the at least one parameter.
  • the at least one parameter includes a dynamic motion blending strength 316
  • the dynamic motion blending strength 316 may be applied to an aligned frame 310 during the temporal filtering process 314.
  • the motion aligned image frame 310 may be generated based on the motion map 306 and the first image frame 302 to align objects that are moving within the first image frame 302 with positions of the objects in a previous image frame 304.
  • the dynamic spatial blending strength 318 may be applied to a denoised frame 312 during the temporal filtering process 314.
  • the denoised frame 312 may be generated to correct for spatial noise within the image frame 302.
  • the corrected image frame 320 may be determined by temporally filtering the first image frame 302 on a per-pixel basis (e.g., by applying the temporal filtering process 314 individually to each pixel of the image frame 302) .
  • Output image frames 230, 330 may then be determined based on the corrected image frame 320.
  • Image frames 230 may be determined by the processor 104 or ISP 112 and stored in memory 106.
  • the stored image frames may be read by the processor 104 and used to form a preview display on a display of the device 100 and/or processed to form a photograph for storage in memory 106 and/or transmission to another device.
  • FIG. 5 is a block diagram illustrating an example processor configuration for image data processing in an image capture device according to one or more embodiments of the disclosure.
  • the processor 104 or other processing circuitry, may be configured to operate on image data to perform one or more operations of the method of Figure 4.
  • the image data may be processed to determine one or more output image frames 510.
  • the processor 104 implements an image map generator 502, a parameter generator 504, and a corrected image generator 506.
  • the processor 104 is configured to receive image data.
  • the first image data may represent a first image frame.
  • the image data may be captured by an image sensor.
  • the image map generator 502 may be configured to determine at least one of a gain map for the first image frame and a motion map for the first image frame. For example, the image map generator 502 may compare the first image frame to a second image frame to compute a motion map for the first image frame. In particular, the motion map may be computed to indicate movement of one or more objects within the first image frame relative to the second image frame and/or movement of the device 100 used to capture the first and second image frames.
  • the image map generator 502 may be determine the gain map based on luminance values within the first image frame. In particular, the image map generator 502 may generate gain values to correct for luminance differences in portions of the first image frame and may add the gain values to corresponding portions of the gain map.
  • the parameter generator 504 may determine one or more parameters for a temporal filtering process. For example, the parameter generator 504 may determine a dynamic motion blending strength for the temporal filtering process based on the motion map. Additionally or alternatively, the parameter generator 504 may determine a dynamic spatial blending strength for the temporal filtering process based on the gain map.
  • the corrected image generator 506 may be configured to determine a corrected image frame for the first image frame by applying the temporal filtering process according to the one or more parameters determined by the parameter generator 504. For example, the dynamic motion blending strength may alter how strongly an aligned frame is blended into the corrected image frame. As another example, the dynamic spatial blending strength may alter how strongly a denoised frame is blended into the corrected image. In certain instances, the corrected image generator 506 may apply the temporal filtering process to the first image frame on a per-pixel basis. The corrected image frame may then be added to the output image frames 510.
  • supporting image processing may include additional aspects, such as any single aspect or any combination of aspects described below or in connection with one or more other processes or devices described elsewhere herein.
  • supporting image processing may include an apparatus configured to receive image data including a first image frame; determine at least one of a gain map for the first image frame or a motion map for the first image frame; determine at least one parameter for a temporal filtering process based on the at least one of the gain map and the motion map; and determine a corrected image frame by temporally filtering the first image frame based on the at least one parameter.
  • the apparatus may perform or operate according to one or more aspects as described below.
  • the apparatus includes a wireless device, such as a UE.
  • the apparatus includes a remote server, such as a cloud-based computing solution, which receives image data for processing to determine output image frames.
  • the apparatus may include at least one processor, and a memory coupled to the processor.
  • the processor may be configured to perform operations described herein with respect to the apparatus.
  • the apparatus may include a non-transitory computer-readable medium having program code recorded thereon and the program code may be executable by a computer for causing the computer to perform operations described herein with reference to the apparatus.
  • the apparatus may include one or more means configured to perform operations described herein.
  • a method of wireless communication may include one or more operations described herein with reference to the apparatus.
  • determining the at least one of a gain map or a motion map includes determining a motion map, wherein receiving the image data further includes a second image frame, and wherein determining the motion map includes comparing the first image frame to the second image frame to generate the motion map.
  • determining the motion map includes determining a difference in location for at least a portion of the first image frame relative to the second image frame.
  • determining the motion map includes determining a non-zero value for a portion of the motion map corresponding to movement between the first image frame and the second image frame exceeding a predetermined threshold.
  • the instructions further cause the processor to determine an aligned frame based on the motion map and the first image frame, wherein the aligned frame corrects for movement distortion within the first image frame.
  • the at least one parameter includes a dynamic motion blending strength based on the motion map, and wherein the dynamic motion blending strength is applied to the aligned frame relative to the first image frame during the temporal filtering process.
  • the instructions further cause the processor to determine, according to a spatial denoising process, a denoised frame that removes spatial noise from the first image frame.
  • the at least one parameter includes a dynamic spatial blending strength based on the gain map, and wherein the dynamic spatial blending strength is applied to the denoised frame relative to the first image frame during the temporal filtering process.
  • determining the gain map is based on luminance values within the first image frame.
  • the at least one parameter is determined on a per-pixel basis for the first image frame, and wherein determining the corrected image frame includes temporally filtering the first image frame on a per-pixel basis.
  • supporting image processing may include a method that includes receiving image data including a first image frame; determining at least one of a gain map for the first image frame or a motion map for the first image frame; determining at least one parameter for a temporal filtering process based on the at least one of the gain map and the motion map; and determining a corrected image frame by temporally filtering the first image frame based on the at least one parameter.
  • determining the at least one of a gain map or a motion map includes determining a motion map, wherein receiving the image data further includes a second image frame, and wherein determining the motion map includes comparing the first image frame to the second image frame to generate the motion map.
  • determining the motion map includes determining a difference in location for at least a portion of the first image frame relative to the second image frame.
  • determining the motion map includes determining a non-zero value for a portion of the motion map corresponding to movement between the first image frame and the second image frame exceeding a predetermined threshold.
  • the instructions further cause the processor to determine an aligned frame based on the motion map and the first image frame, wherein the aligned frame corrects for movement distortion within the first image frame.
  • the at least one parameter includes a dynamic motion blending strength based on the motion map, and wherein the dynamic motion blending strength is applied to the aligned frame relative to the first image frame during the temporal filtering process.
  • the instructions further cause the processor to determine, according to a spatial denoising process, a denoised frame that removes spatial noise from the first image frame.
  • the at least one parameter includes a dynamic spatial blending strength based on the gain map, and wherein the dynamic spatial blending strength is applied to the denoised frame relative to the first image frame during the temporal filtering process.
  • determining the gain map is based on luminance values within the first image frame.
  • the at least one parameter is determined on a per-pixel basis for the first image frame, and wherein determining the corrected image frame includes temporally filtering the first image frame on a per-pixel basis.
  • supporting image processing includes a method that includes receiving image data including a first image frame; determining a motion map for the first image frame that identifies movement within the first image frame; determining an aligned frame based on the motion map and the first image frame, wherein the aligned frame corrects for movement distortion within the first image frame; determining a dynamic motion blending strength based on the motion map; and determining a corrected image frame by temporally filtering the first image frame and the aligned frame based on the dynamic motion blending strength.
  • the image data further includes a second image frame
  • determining the motion map includes comparing the first image frame to the second image frame to generate the motion map
  • the motion map is generated to reflect movement within the first image frame relative to the second image frame.
  • determining the motion map includes determining a non-zero value for a portion of the motion map corresponding to movement between the first image frame and the second image frame exceeding a predetermined threshold.
  • the method further includes determining a gain map for the first image frame; determining, according to a spatial denoising process, a denoised frame that removes spatial noise from the first image frame; and determining a dynamic spatial blending strength based on the gain map, wherein determining the corrected image frame further includes temporally filtering the first image frame and the denoised frame based on the dynamic spatial blending strength.
  • supporting image processing includes an apparat that includes a memory storing processor-readable code; and at least one processor coupled to the memory.
  • the at least one processor may be configured to execute the processor-readable code to cause the at least one processor to perform operations including: receiving image data including a first image frame; determining a motion map for the first image frame that identifies movement within the first image frame; determining an aligned frame based on the motion map and the first image frame, wherein the aligned frame corrects for movement distortion within the first image frame; determining a dynamic motion blending strength based on the motion map; and determining a corrected image frame by temporally filtering the first image frame and the aligned frame based on the dynamic motion blending strength.
  • the image data further includes a second image frame, and wherein determining the motion map includes comparing the first image frame to the second image frame to generate the motion map.
  • the motion map is generated to reflect movement within the first image frame relative to the second image frame.
  • determining the motion map includes determining a non-zero value for a portion of the motion map corresponding to movement between the first image frame and the second image frame exceeding a predetermined threshold.
  • the operations further include: determining a gain map for the first image frame; determining, according to a spatial denoising process, a denoised frame that removes spatial noise from the first image frame; and determining a dynamic spatial blending strength based on the gain map, and wherein determining the corrected image frame further includes temporally filtering the first image frame and the denoised frame based on the dynamic spatial blending strength.
  • Components, the functional blocks, and the modules described herein with respect to FIGs. 1-5 include processors, electronics devices, hardware devices, electronics components, logical circuits, memories, software codes, firmware codes, among other examples, or any combination thereof.
  • Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, application, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, and/or functions, among other examples, whether referred to as software, firmware, middleware, microcode, hardware description language or otherwise.
  • features discussed herein may be implemented via specialized processor circuitry, via executable instructions, or combinations thereof.
  • FIGs. 4 and 5 may be combined with one or more blocks (or operations) described with reference to another of the figures.
  • one or more blocks (or operations) of FIG. 4 may be combined with one or more blocks (or operations) of FIGs. 1-3.
  • one or more blocks associated with FIG. 5 may be combined with one or more blocks (or operations) associated with FIGs. 1-3.
  • the hardware and data processing apparatus used to implement the various illustrative logics, logical blocks, modules and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose single-or multi-chip processor, a digital signal processor (DSP) , an application specific integrated circuit (ASIC) , a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a general-purpose processor may be a microprocessor, or any conventional processor, controller, microcontroller, or state machine.
  • a processor may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • particular processes and methods may be performed by circuitry that is specific to a given function.
  • the functions described may be implemented in hardware, digital electronic circuitry, computer software, firmware, including the structures disclosed in this specification and their structural equivalents thereof, or in any combination thereof. Implementations of the subject matter described in this specification also may be implemented as one or more computer programs, which is one or more modules of computer program instructions, encoded on a computer storage media for execution by, or to control the operation of, data processing apparatus.
  • Computer-readable media includes both computer storage media and communication media including any medium that may be enabled to transfer a computer program from one place to another.
  • a storage media may be any available media that may be accessed by a computer.
  • Such computer-readable media may include random-access memory (RAM) , read-only memory (ROM) , electrically erasable programmable read-only memory (EEPROM) , CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection may be properly termed a computer-readable medium.
  • Disk and disc includes compact disc (CD) , laser disc, optical disc, digital versatile disc (DVD) , floppy disk, and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.
  • drawings may schematically depict one or more example processes in the form of a flow diagram. However, other operations that are not depicted may be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations may be performed before, after, simultaneously, or between any of the illustrated operations. In certain circumstances, multitasking and parallel processing may be advantageous.
  • the term “or, ” when used in a list of two or more items means that any one of the listed items may be employed by itself, or any combination of two or more of the listed items may be employed. For example, if a composition is described as containing components A, B, or C, the composition may contain A alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination.
  • substantially is defined as largely, but not necessarily wholly, what is specified (and includes what is specified; for example, substantially 90 degrees includes 90 degrees and substantially parallel includes parallel) , as understood by a person of ordinary skill in the art. In any disclosed implementations, the term “substantially” may be substituted with “within [a percentage] of” what is specified, where the percentage includes . 1, 1, 5, or 10 percent.

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Abstract

La présente divulgation concerne des systèmes, des procédés et des dispositifs de traitement de signal d'image qui prennent en charge un filtrage temporel amélioré de trames d'image visant à corriger des distorsions de mouvement. Selon un premier aspect, un procédé de traitement d'image consiste à recevoir des données d'image contenant une première trame d'image. Un ou plusieurs éléments parmi (i) une carte de gain pour la première trame d'image et/ou (ii) une carte de mouvement pour la première trame d'image peuvent être calculés et peuvent être utilisés pour déterminer au moins un paramètre pour un processus de filtrage temporel. Une trame d'image corrigée peut être déterminée par filtrage temporel de la première trame d'image sur la base dudit au moins un paramètre. L'invention concerne et revendique également d'autres aspects et caractéristiques.
PCT/CN2022/138625 2022-12-13 2022-12-13 Filtrage temporel pour correction de distorsions de mouvement WO2024124395A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1665298A (zh) * 2003-12-11 2005-09-07 三星电子株式会社 从数字运动画面数据中去除噪声的方法
CN102640184A (zh) * 2009-10-20 2012-08-15 苹果公司 用于图像信号处理的时域滤波技术
US20160037061A1 (en) * 2014-07-31 2016-02-04 Apple Inc. Dynamic motion estimation and compensation for temporal filtering
CN106534833A (zh) * 2016-12-07 2017-03-22 上海大学 一种联合空间时间轴的双视点立体视频稳定方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1665298A (zh) * 2003-12-11 2005-09-07 三星电子株式会社 从数字运动画面数据中去除噪声的方法
CN102640184A (zh) * 2009-10-20 2012-08-15 苹果公司 用于图像信号处理的时域滤波技术
US20160037061A1 (en) * 2014-07-31 2016-02-04 Apple Inc. Dynamic motion estimation and compensation for temporal filtering
CN106534833A (zh) * 2016-12-07 2017-03-22 上海大学 一种联合空间时间轴的双视点立体视频稳定方法

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