Patent Publication Number: US-11653088-B2

Title: Three-dimensional noise reduction

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims the benefit of U.S. Provisional Application No. 62/341,580, filed on May 25, 2016, which is incorporated herein by reference in its entirety. This application claims the benefit of U.S. Provisional Application No. 62/341,415, filed on May 25, 2016, which is incorporated herein by reference in its entirety. This application claims the benefit of U.S. patent application Ser. No. 15/268,038, filed on Sep. 16, 2016, which is incorporated herein by reference in its entirety. This application claims the benefit of U.S. patent application Ser. No. 15/358,495, filed on Nov. 22, 2016, which is incorporated herein by reference in its entirety. This application claims the benefit of U.S. patent application Ser. No. 15/399,269, filed on Jan. 5, 2017, which is incorporated herein by reference in its entirety. This application is a divisional of U.S. patent application Ser. No. 16/303,892, filed on Nov. 21, 2018, which is a national phase under 35 U.S.C. § 371 of PCT Application No. PCT/US2017/034231, filed on May 24, 2017, which are incorporated herein by reference in its entirety. 
    
    
     COPYRIGHT 
     A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. 
     TECHNICAL FIELD 
     The present disclosure relates to digital image and video processing. 
     BACKGROUND 
     Image capture devices, such as cameras, may capture content as images or video. Light may be received and focused via a lens and may be converted to an electronic image signal by an image sensor. The image signal may be processed by an image signal processor (ISP) to form an image, which may be stored and/or encoded. In some implementations, multiple images or video frames may include spatially adjacent or overlapping content. Accordingly, systems, methods, and apparatus for capturing, processing, and/or encoding images, video, or both may be advantageous. 
     SUMMARY 
     The present disclosure describes, inter alia, apparatus and methods for digital image and video processing. 
     In a first aspect, the subject matter described in this specification can be embodied in systems that include an image sensor configured to capture video and a processing apparatus that is configured to: receive a current frame of video from the image sensor; combine the current frame with a recirculated frame to obtain a noise reduced frame, where the recirculated frame is based on one or more previous frames of video from the image sensor, and in which the current frame is combined with the recirculated frame using a set of mixing weights for respective image portions of the recirculated frame; determine a noise map for the noise reduced frame, where the noise map is determined based on estimates of noise levels for pixels in the current frame, a noise map for the recirculated frame, and the set of mixing weights; recirculate the noise map with the noise reduced frame to combine the noise reduced frame with a next frame of video from the image sensor; and store, display, or transmit an output video that is based on the noise reduced frame. 
     In a second aspect, the subject matter described in this specification can be embodied in methods that include receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor, and in which the current image is combined with the recirculated image using a set of mixing weights for respective image portions of the recirculated image; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and the set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image. 
     In a third aspect, the subject matter described in this specification can be embodied in systems that include an image sensor configured to capture image data using a plurality of selectable exposure times; and a processing apparatus that is configured to: receive a first image from the image sensor, where the first image is captured with a first exposure time; receive a second image from the image sensor, where the second image is captured with a second exposure time that is less than the first exposure time; determine a high dynamic range image based on the first image in a raw format and the second image in a raw format, in which an image portion of the high dynamic range image is based on a corresponding image portion of the second image when a pixel of a corresponding image portion of the first image is saturated; and store, display, or transmit an output image that is based on the high dynamic range image. 
     In a fourth aspect, the subject matter described in this specification can be embodied in a non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, facilitate performance of operations, including: obtaining, by an image signal processor, a target image; obtaining, by the image signal processor, a reference image; obtaining motion compensation information indicating motion identified between the reference image and the target image, wherein obtaining the motion compensation information includes obtaining local motion compensation information and obtaining global motion compensation information; obtaining a processed image by updating the target image based on the motion compensation information; and outputting the processed image. 
     In a fifth aspect, the subject matter described in this specification can be embodied in methods that include: obtaining, by an image signal processor, a target image; obtaining, by the image signal processor, a reference image; obtaining motion compensation information indicating motion identified between the reference image and the target image, wherein obtaining the motion compensation information includes obtaining local motion compensation information and obtaining global motion compensation information; obtaining a processed image by updating the target image based on the motion compensation information; and outputting the processed image. 
     In a sixth aspect, the subject matter described in this specification can be embodied in an image capture apparatus including one or more image sensors configured to capture input video, and an image signal processor configured to: obtain, by an image signal processor, a target image; obtain, by the image signal processor, a reference image; obtain motion compensation information indicating motion identified between the reference image and the target image, wherein obtaining the motion compensation information includes obtaining local motion compensation information and obtaining global motion compensation information; obtain a processed image by updating the target image based on the motion compensation information; and output the processed image. 
     In a seventh aspect, the subject matter described in this specification can be embodied in methods that include receiving a current frame of video from an image sensor; combining the current frame with a recirculated frame to obtain a noise reduced frame, where the recirculated frame is based on one or more previous frames of video from the image sensor, and in which the current frame is combined with the recirculated frame using a set of mixing weights for respective image portions of the recirculated frame; determining a noise map for the noise reduced frame, where the noise map is determined based on estimates of noise levels for pixels in the current frame, a noise map for the recirculated frame, and the set of mixing weights; recirculating the noise map with the noise reduced frame to combine the noise reduced frame with a next frame of video from the image sensor; and storing, displaying, or transmitting an output video that is based on the noise reduced frame. 
     In a eight aspect, the subject matter described in this specification can be embodied in systems that include an image sensor configured to capture a sequence of images and a processing apparatus that is configured to: receive a current image of the sequence of images from the image sensor; combine the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor, and in which the current image is combined with the recirculated image using a set of mixing weights for respective image portions of the recirculated image; determine a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and the set of mixing weights; recirculate the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and store, display, or transmit an output image that is based on the noise reduced image. 
     In a ninth aspect, the subject matter described in this specification can be embodied in a non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, facilitate performance of operations, including: receiving a current frame of video from an image sensor; combining the current frame with a recirculated frame to obtain a noise reduced frame, where the recirculated frame is based on one or more previous frames of video from the image sensor, and in which the current frame is combined with the recirculated frame using a set of mixing weights for respective image portions of the recirculated frame; determining a noise map for the noise reduced frame, where the noise map is determined based on estimates of noise levels for pixels in the current frame, a noise map for the recirculated frame, and the set of mixing weights; recirculating the noise map with the noise reduced frame to combine the noise reduced frame with a next frame of video from the image sensor; and storing, displaying, or transmitting an output video that is based on the noise reduced frame. 
     In a tenth aspect, the subject matter described in this specification can be embodied in a non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, facilitate performance of operations, including: receiving a current image of a sequence of images from an image sensor; combining the current image with a recirculated image to obtain a noise reduced image, where the recirculated image is based on one or more previous images of the sequence of images from the image sensor, and in which the current image is combined with the recirculated image using a set of mixing weights for respective image portions of the recirculated image; determining a noise map for the noise reduced image, where the noise map is determined based on estimates of noise levels for pixels in the current image, a noise map for the recirculated image, and the set of mixing weights; recirculating the noise map with the noise reduced image to combine the noise reduced image with a next image of the sequence of images from the image sensor; and storing, displaying, or transmitting an output image that is based on the noise reduced image. 
     In an eleventh aspect, the subject matter described in this specification can be embodied in a non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, facilitate performance of operations, including: receiving a first image from an image sensor, where the first image is captured with a first exposure time; receiving a second image from the image sensor, where the second image is captured with a second exposure time that is less than the first exposure time; determining a high dynamic range image based on the first image in a raw format and the second image in a raw format, in which an image portion of the high dynamic range image is based on a corresponding image portion of the second image when a pixel of a corresponding image portion of the first image is saturated; and storing, displaying, or transmitting an output image that is based on the high dynamic range image. 
     In a twelfth aspect, the subject matter described in this specification can be embodied in methods that include: receiving a first image from an image sensor, where the first image is captured with a first exposure time; receiving a second image from the image sensor, where the second image is captured with a second exposure time that is less than the first exposure time; determining a high dynamic range image based on the first image in a raw format and the second image in a raw format, in which an image portion of the high dynamic range image is based on a corresponding image portion of the second image when a pixel of a corresponding image portion of the first image is saturated; and storing, displaying, or transmitting an output image that is based on the high dynamic range image. 
     These and other objects, features, and characteristics of the system and/or method disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the disclosure. As used in the specification and in the claims, the singular forms of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosed embodiments have other advantages and features which will be more readily apparent from the detailed description, the appended claims, and the accompanying figures. A brief introduction of the figures is below. 
         FIG.  1    is a diagram of an example of an image capture system for content capture in accordance with implementations of this disclosure. 
         FIG.  2    is a block diagram of an example of an image capture device in accordance with implementations of this disclosure. 
         FIG.  3    is a cross-sectional view of an example of an image capture apparatus including overlapping fields-of-view in accordance with implementations of this disclosure. 
         FIG.  4    is a block diagram of an example of an image processing and coding pipeline in accordance with implementations of this disclosure. 
         FIG.  5    is a functional block diagram of an example of an image signal processor in accordance with implementations of this disclosure. 
         FIG.  6 A  is a block diagram of an example of a system configured for image capture. 
         FIG.  6 B  is a block diagram of an example of a system configured for image capture. 
         FIG.  7    is a block diagram of an example of an image processing pipeline for capturing images and reducing noise in the images. 
         FIG.  8    is a block diagram of an example of an image processing pipeline for capturing images with high dynamic range and reducing noise in the high dynamic range images. 
         FIG.  9    is a flowchart of an example of a technique for applying three-dimensional noise reduction to captured images. 
         FIG.  10    is a flowchart of an example of a technique for determining mixing weights for temporal noise reduction. 
         FIG.  11    is a flowchart of an example of a technique for applying temporal noise reduction to high dynamic range images. 
         FIG.  12 A  is a flowchart of an example of a technique for determining a high dynamic range image based on images captured with different exposure times. 
         FIG.  12 B  is a flowchart of an example of a technique for determining a blending ratio for an image portion of a high dynamic range image. 
         FIG.  13 A  is a flowchart of an example of a technique for recirculating a noise map with a noise reduced image. 
         FIG.  13 B  is a flowchart of an example of a technique for applying motion compensation to a recirculated image. 
         FIG.  13 C  is a flowchart of an example of a technique for obtaining local motion information for a reference image and a target image. 
         FIG.  14    is a diagram of an example of a target image and an example of a corresponding one-half resolution downscaled image. 
         FIG.  15    is a diagram of an example of a one-quarter resolution downscaled image and an example of a one-eighth resolution downscaled image. 
         FIG.  16    is a diagram of an example of a one-sixteenth resolution downscaled image and an example of a one-thirty-second resolution downscaled image. 
         FIG.  17    is a flowchart of an example of a technique for motion compensation. 
         FIG.  18    is a flowchart of an example of a technique for determining whether to use local motion compensation information or global motion compensation information. 
         FIG.  19    is a flowchart of an example of a technique for obtaining local motion information. 
         FIG.  20    is a flowchart of an example of a technique for obtaining local motion information for a current image. 
         FIG.  21    illustrates an example of an architecture for processing and stitching images captured with multiple image sensors. 
         FIG.  22    illustrates overlapping images captured with multiple image sensors. 
         FIG.  23    illustrates an example of a technique for stitching images captured with multiple image sensors. 
         FIG.  24 A  illustrates images captured with a cubic array of image sensors. 
         FIG.  24 B  illustrates a two dimensional grid of images captured by a cubic array of image sensors. 
     
    
    
     All figures disclosed herein are © Copyright  2021  GoPro Inc. All rights reserved. 
     DETAILED DESCRIPTION 
     Content, such as visual content, may be captured as one or more images or video frames by one or more image capture devices, such as a camera or camera array. An image capture device may include one or more lenses, image sensors, image signal processors, encoders, or combinations thereof. A lens may receive and focus light on an image sensor or sensors. An image sensor or sensors may sample the light and generate an electronic image signal. An image signal processor (ISP) may receive the image signal from one or more sensors and may process the image signal to generate an image, picture, or frame. The generated images may be stored, such as in a memory of an image e capture device, and/or sent to an encoder for encoding, which may include compression. 
     Three-dimensional noise reduction processing may be implemented to reduce noise levels (e.g., standard deviation, variance, or signal-to-noise-ratio) in pixel values in a sequence of captured images (e.g., frames of video) and improve the quality of the captured images. Three-dimensional noise reduction processing may include temporal noise reduction processing, which combines (e.g., using weighted averages) pixel values for an incoming current image with pixel values for corresponding pixels of a recirculated image that may be based on (e.g., via recursive processing of incoming current images in the sequence of images) one or more previous images in the sequence of images. Whether and/or how significantly an image portion (e.g., a pixel or block of pixels) of the recirculated image is combined with the current image may be determined (e.g., by determining mixing weights for respective image portions) based on an assessment as to how well the image portion corresponds to an image portion of the current image at the same spatial location. For example, estimates of the noise level for the pixel value(s) in the image portion of the recirculated image and/or estimates of the noise level for the pixel value(s) in the image portion of the current image may be used to determine a mixing weight (e.g., set to zero if the image portion is not used or to a positive number less than one if the image portion is used for temporal noise reduction) for the respective image portion of the recirculated image. In some implementations, the estimates of noise level for image portions the recirculated image are stored in a noise map that includes locations for the respective image portions of the recirculated image. A noise map for a noise reduced image (e.g., an image resulting from combination of the current image with the recirculated image) may be determined based on the estimates of noise levels for the current image, estimates of noise level for the recirculated image (e.g., from a previous noise map), and a set of mixing weights that is used to determine the noise reduced image. The resulting noise map may be recirculated (e.g., fed back) with noise reduced image for combination with a next current image in the sequence of the images. Having access to image portion (e.g., pixel or block of pixel) resolution estimates of noise for the recirculated image may facilitate improved combination with incoming current images to reduce noise levels and improve image quality in the captured images. 
     High dynamic range processing may consist of capturing several images of the same scene (e.g., in quick succession or partially overlapping in time) with different exposure times and then fusing these images so that dark parts of the image can be taken from the image with longest exposure time (for which a noise level is smallest) and bright parts of the image can be taken from the image with a shorter exposure time (e.g., where the longer exposure time image has pixel values that are saturated). In some implementations, more than images with different exposure time are captured, and parts of the images with intermediate brightness are taken from intermediate images (e.g., the image which has the longest exposure time while not exhibiting pixel value saturation). For example, high dynamic range processing may include a fusion algorithm taking N images as input and providing a single image at the output. In some implementations, image portions of constituent images are combined to form a high dynamic range image using a blending ratio map that specifies which image(s) individual image portions (e.g., pixels or blocks of pixels) will be based on. For example, image portions from multiple (e.g., two) constituent images may be blended (e.g., using a respective positive blending ratio less than one) to determine a corresponding image portion of the high dynamic range image. For example, a positive blending ratio less than one may be determined when a pixel value for a respective image portion is in a range near a saturation level. For example, a low-pass spatial filter may be applied to a blending ration map to smooth a combination of the constituent image. 
     High dynamic range processing may be performed (e.g., by an image signal processor or before passing the captured images to an image signal processor) in raw domain—instead of performing this processing in the YUV domain. Some advantages of performing the high dynamic range processing in the raw domain (early in an image signal processing pipeline) may include: (1) Only a high dynamic range image, rather than all N of the constituent images captured with different exposure times, may be processed by the later stages of an image processing pipeline (e.g., an image signal processor as the image signal processor processes the resulting image after high dynamic range fusion). (2) This may result in higher performance and lower power consumption, despite the fact that the later stages of a pipeline (e.g., an image signal processor) must use a wider dynamic (e.g., downstream processing may be carried out on 16 bits instead of 14). For example, processing a single 16 bit image may consume less processing resources (e.g., processor cycles and memory) than two 14 bits images. (3) High dynamic range image fusion may be performed in linear space, where quantization may have less consequences than in the YUV domain. (4) Tone mapping is simplified as it is done after an histogram computation of the image. Having access to the histogram with the full dynamic improves the quality of tone mapping. 
     High dynamic range processing may include spatial regularization, and thus noise levels for image portions may not be a pure function of the pixel value. This may complicate noise reduction processing (e.g., temporal noise reduction processing and/or three-dimensional noise processing) that depends on noise levels for pixels of incoming current images. To accommodate noise reduction processing occurring later in an image processing pipeline, estimates of noise level for respective image portions (e.g., pixels or Bayer blocks of 4 pixels), which are used by a noise reduction module to filter out noise, may be determined and stored in an input noise map for the high dynamic range image that may be passed into a noise reduction module with a corresponding current high dynamic range image. In some implementations, determining an input noise map for the high dynamic range image has small (e.g., marginal) impact on the consumption of image processing resource, and thus the savings due to downstream processing for a single high dynamic ranges images is substantially preserved jeopardized. 
     Motion compensation may be applied to a reference image to better align image portions (e.g., pixels or blocks of pixels) with corresponding image portions of a target image. For example, a target image may be a current image in a sequence of images that is subject to temporal noise reduction processing, and the reference image may be a recirculated image based on one or more previous images in the sequence of images. For example, a target image may be a constituent image of a high dynamic range image captured with a long exposure time, and the reference image may be a corresponding constituent image that was captured with a short exposure time. In some implementations, a local motion compensation transform is applied to a reference image to determine a first candidate image and a global motion compensation transform is applied to the reference image to determine a second candidate image. Quality metrics that may measure the correspondence of image portions in the respective candidate images with the target image may then be determined and the local motion compensation transformation or the global motion compensation transformation may be selected for use with the reference image. In some implementations, local motion estimation to obtain local motion information (e.g., local motion vectors) includes a multi-scale analysis may be used to iteratively narrow the scope of a block matching search for corresponding blocks in a target image. 
     Motion compensation may be applied to a recirculated image to increase hit rate (e.g., a percentage of image portions of a recirculated image that are used for temporal noise reduction) temporal noise reduction algorithm and improve image quality of the captured images. In some implementations, recirculating an image for temporal noise reduction processing includes applying motion compensation (e.g., local motion compensation and/or global motion compensation) to the recirculated image to better align image portions of the recirculated image with corresponding image portions of a next current image with which it will be combined for temporal noise reduction. A motion compensation transformation applied to a recirculated image may also be applied to a noise map for the recirculated image in order to preserve the correspondence between pixel values in the recirculated image and estimates of noise level in the noise map. 
     In some implementations, multiple image sensors with overlapping fields are used to capture images that are stitched together to obtain a composite image that represents the combined field of view for the multiple image sensors. When writing an overlapping portion of a processed image to memory, one of the overlapping processed image portions may be maintained in an internal memory buffer of an image signal processor while a corresponding overlapping processed image portion for another image sensor is read from memory, combined (e.g., via a blending operation) with the overlapping processed image portion in the buffer, and the combined processed image portion is written back to memory. This architecture for an image processing pipeline for stitching may reduce memory bandwidth usage and improve performance of an image signal processor. 
     Implementations are described in detail with reference to the drawings, which are provided as examples so as to enable those skilled in the art to practice the technology. The figures and examples are not meant to limit the scope of the present disclosure to a single implementation or embodiment, and other implementations and embodiments are possible by way of interchange of, or combination with, some or all of the described or illustrated elements. Wherever convenient, the same reference numbers will be used throughout the drawings to refer to same or like parts. 
       FIG.  1    is a diagram of an example of an image capture system  100  for content capture in accordance with implementations of this disclosure. As shown in  FIG.  1   , an image capture system  100  may include an image capture apparatus  110 , an external user interface (UI) device  120 , or a combination thereof. 
     In some implementations, the image capture apparatus  110  may be a multi-face apparatus and may include multiple image capture devices, such as image capture devices  130 ,  132 ,  134  as shown in  FIG.  1   , arranged in a structure  140 , such as a cube-shaped cage as shown. Although three image capture devices  130 ,  132 ,  134  are shown for simplicity in  FIG.  1   , the image capture apparatus  110  may include any number of image capture devices. For example, the image capture apparatus  110  shown in  FIG.  1    may include six cameras, which may include the three image capture devices  130 ,  132 ,  134  shown and three cameras not shown. 
     In some implementations, the structure  140  may have dimensions, such as between 25 mm and 150 mm. For example, the length of each side of the structure  140  may be 105 mm. The structure  140  may include a mounting port  142 , which may be removably attachable to a supporting structure, such as a tripod, a photo stick, or any other camera mount (not shown). The structure  140  may be a rigid support structure, such that the relative orientation of the image capture devices  130 ,  132 ,  134  of the image capture apparatus  110  may be maintained in relatively static or fixed alignment, except as described herein. 
     The image capture apparatus  110  may obtain, or capture, image content, such as images, video, or both, with a 360° field-of-view, which may be referred to herein as panoramic or spherical content. For example, each of the image capture devices  130 ,  132 ,  134  may include respective lenses, for receiving and focusing light, and respective image sensors for converting the received and focused light to an image signal, such as by measuring or sampling the light, and the multiple image capture devices  130 ,  132 ,  134  may be arranged such that respective image sensors and lenses capture a combined field-of-view characterized by a spherical or near spherical field-of-view. 
     In some implementations, each of the image capture devices  130 ,  132 ,  134  may have a respective field-of-view  170 ,  172 ,  174 , such as a field-of-view  170 ,  172 ,  174  that 90° in a lateral dimension  180 ,  182 ,  184  and includes 120° in a longitudinal dimension  190 ,  192 ,  194 . In some implementations, image capture devices  130 ,  132 ,  134  having overlapping fields-of-view  170 ,  172 ,  174 , or the image sensors thereof, may be oriented at defined angles, such as at 90°, with respect to one another. In some implementations, the image sensor of the image capture device  130  is directed along the X axis, the image sensor of the image capture device  132  is directed along the Y axis, and the image sensor of the image capture device  134  is directed along the Z axis. The respective fields-of-view  170 ,  172 ,  174  for adjacent image capture devices  130 ,  132 ,  134  may be oriented to allow overlap for a stitching function. For example, the longitudinal dimension  190  of the field-of-view  170  for the image capture device  130  may be oriented at 90° with respect to the latitudinal dimension  184  of the field-of-view  174  for the image capture device  134 , the latitudinal dimension  180  of the field-of-view  170  for the image capture device  130  may be oriented at 90° with respect to the longitudinal dimension  192  of the field-of-view  172  for the image capture device  132 , and the latitudinal dimension  182  of the field-of-view  172  for the image capture device  132  may be oriented at 90° with respect to the longitudinal dimension  194  of the field-of-view  174  for the image capture device  134 . 
     The image capture apparatus  110  shown in  FIG.  1    may have 420° angular coverage in vertical and/or horizontal planes by the successive overlap of 90°, 120°, 90°, 120° respective fields-of-view  170 ,  172 ,  174  (not all shown) for four adjacent image capture devices  130 ,  132 ,  134  (not all shown). For example, fields-of-view  170 ,  172  for the image capture devices  130 ,  132  and fields-of-view (not shown) for two image capture devices (not shown) opposite the image capture devices  130 ,  132  respectively may be combined to provide 420° angular coverage in a horizontal plane. In some implementations, the overlap between fields-of-view of image capture devices  130 ,  132 ,  134  having a combined field-of-view including less than 360° angular coverage in a vertical and/or horizontal plane may be aligned and merged or combined to produce a panoramic image. For example, the image capture apparatus  110  may be in motion, such as rotating, and source images captured by at least one of the image capture devices  130 ,  132 ,  134  may be combined to form a panoramic image. As another example, the image capture apparatus  110  may be stationary, and source images captured contemporaneously by each image capture device  130 ,  132 ,  134  may be combined to form a panoramic image. 
     In some implementations, an image capture device  130 ,  132 ,  134  may include a lens  150 ,  152 ,  154  or other optical element. An optical element may include one or more lens, macro lens, zoom lens, special-purpose lens, telephoto lens, prime lens, achromatic lens, apochromatic lens, process lens, wide-angle lens, ultra-wide-angle lens, fisheye lens, infrared lens, ultraviolet lens, perspective control lens, other lens, and/or other optical element. In some implementations, a lens  150 ,  152 ,  154  may be a fisheye lens and produce fisheye, or near-fisheye, field-of-view images. For example, the respective lenses  150 ,  152 ,  154  of the image capture devices  130 ,  132 ,  134  may be fisheye lenses. In some implementations, images captured by two or more image capture devices  130 ,  132 ,  134  of the image capture apparatus  110  may be combined by stitching or merging fisheye projections of the captured images to produce an equirectangular planar image. For example, a first fisheye image may be a round or elliptical image, and may be transformed to a first rectangular image, a second fisheye image may be a round or elliptical image, and may be transformed to a second rectangular image, and the first and second rectangular images may be arranged side-by-side, which may include overlapping, and stitched together to form the equirectangular planar image. 
     Although not expressly shown in  FIG.  1   , In some implementations, an image capture device  130 ,  132 ,  134  may include one or more image sensors, such as a charge-coupled device (CCD) sensor, an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) sensor, an N-type metal-oxide-semiconductor (NMOS) sensor, and/or any other image sensor or combination of image sensors. 
     Although not expressly shown in  FIG.  1   , In some implementations, an image capture apparatus  110  may include one or more microphones, which may receive, capture, and record audio information, which may be associated with images acquired by the image sensors. 
     Although not expressly shown in  FIG.  1   , the image capture apparatus  110  may include one or more other information sources or sensors, such as an inertial measurement unit (IMU), a global positioning system (GPS) receiver component, a pressure sensor, a temperature sensor, a heart rate sensor, or any other unit, or combination of units, that may be included in an image capture apparatus. 
     In some implementations, the image capture apparatus  110  may interface with or communicate with an external device, such as the external user interface (UI) device  120 , via a wired (not shown) or wireless (as shown) computing communication link  160 . Although a single computing communication link  160  is shown in  FIG.  1    for simplicity, any number of computing communication links may be used. Although the computing communication link  160  shown in  FIG.  1    is shown as a direct computing communication link, an indirect computing communication link, such as a link including another device or a network, such as the internet, may be used. In some implementations, the computing communication link  160  may be a Wi-Fi link, an infrared link, a Bluetooth (BT) link, a cellular link, a ZigBee link, a near field communications (NFC) link, such as an ISO/IEC 23243 protocol link, an Advanced Network Technology interoperability (ANT+) link, and/or any other wireless communications link or combination of links. In some implementations, the computing communication link  160  may be an HDMI link, a USB link, a digital video interface link, a display port interface link, such as a Video Electronics Standards Association (VESA) digital display interface link, an Ethernet link, a Thunderbolt link, and/or other wired computing communication link. 
     In some implementations, the user interface device  120  may be a computing device, such as a smartphone, a tablet computer, a phablet, a smart watch, a portable computer, and/or another device or combination of devices configured to receive user input, communicate information with the image capture apparatus  110  via the computing communication link  160 , or receive user input and communicate information with the image capture apparatus  110  via the computing communication link  160 . 
     In some implementations, the image capture apparatus  110  may transmit images, such as panoramic images, or portions thereof, to the user interface device  120  via the computing communication link  160 , and the user interface device  120  may store, process, display, or a combination thereof the panoramic images. 
     In some implementations, the user interface device  120  may display, or otherwise present, content, such as images or video, acquired by the image capture apparatus  110 . For example, a display of the user interface device  120  may be a viewport into the three-dimensional space represented by the panoramic images or video captured or created by the image capture apparatus  110 . 
     In some implementations, the user interface device  120  may communicate information, such as metadata, to the image capture apparatus  110 . For example, the user interface device  120  may send orientation information of the user interface device  120  with respect to a defined coordinate system to the image capture apparatus  110 , such that the image capture apparatus  110  may determine an orientation of the user interface device  120  relative to the image capture apparatus  110 . Based on the determined orientation, the image capture apparatus  110  may identify a portion of the panoramic images or video captured by the image capture apparatus  110  for the image capture apparatus  110  to send to the user interface device  120  for presentation as the viewport. In some implementations, based on the determined orientation, the image capture apparatus  110  may determine the location of the user interface device  120  and/or the dimensions for viewing of a portion of the panoramic images or video. 
     In an example, a user may rotate (sweep) the user interface device  120  through an arc or path  122  in space, as indicated by the arrow shown at  122  in  FIG.  1   . The user interface device  120  may communicate display orientation information to the image capture apparatus  110  using a communication interface such as the computing communication link  160 . The image capture apparatus  110  may provide an encoded bitstream to enable viewing of a portion of the panoramic content corresponding to a portion of the environment of the display location as the image capture apparatus  110  traverses the path  122 . Accordingly, display orientation information from the user interface device  120  may be transmitted to the image capture apparatus  110  to control user selectable viewing of captured images and/or video. 
     In some implementations, the image capture apparatus  110  may communicate with one or more other external devices (not shown) via wired or wireless computing communication links (not shown). 
     In some implementations, data, such as image data, audio data, and/or other data, obtained by the image capture apparatus  110  may be incorporated into a combined multimedia stream. For example, the multimedia stream may include a video track and/or an audio track. As another example, information from various metadata sensors and/or sources within and/or coupled to the image capture apparatus  110  may be processed to produce a metadata track associated with the video and/or audio track. The metadata track may include metadata, such as white balance metadata, image sensor gain metadata, sensor temperature metadata, exposure time metadata, lens aperture metadata, bracketing configuration metadata and/or other parameters. In some implementations, a multiplexed stream may be generated to incorporate a video and/or audio track and one or more metadata tracks. 
     In some implementations, the user interface device  120  may implement or execute one or more applications, such as GoPro Studio, GoPro App, or both, to manage or control the image capture apparatus  110 . For example, the user interface device  120  may include an application for controlling camera configuration, video acquisition, video display, or any other configurable or controllable aspect of the image capture apparatus  110 . 
     In some implementations, the user interface device  120 , such as via an application (e.g., GoPro App), may generate and share, such as via a cloud-based or social media service, one or more images, or short video clips, such as in response to user input. 
     In some implementations, the user interface device  120 , such as via an application (e.g., GoPro App), may remotely control the image capture apparatus  110 , such as in response to user input. 
     In some implementations, the user interface device  120 , such as via an application (e.g., GoPro App), may display unprocessed or minimally processed images or video captured by the image capture apparatus  110  contemporaneously with capturing the images or video by the image capture apparatus  110 , such as for shot framing, which may be referred to herein as a live preview, and which may be performed in response to user input. 
     In some implementations, the user interface device  120 , such as via an application (e.g., GoPro App), may mark one or more key moments contemporaneously with capturing the images or video by the image capture apparatus  110 , such as with a HiLight Tag, such as in response to user input. 
     In some implementations, the user interface device  120 , such as via an application (e.g., GoPro App), may display, or otherwise present, marks or tags associated with images or video, such as HiLight Tags, such as in response to user input. For example, marks may be presented in a GoPro Camera Roll application for location review and/or playback of video highlights. 
     In some implementations, the user interface device  120 , such as via an application (e.g., GoPro App), may wirelessly control camera software, hardware, or both. For example, the user interface device  120  may include a web-based graphical interface accessible by a user for selecting a live or previously recorded video stream from the image capture apparatus  110  for display on the user interface device  120 . 
     In some implementations, the user interface device  120  may receive information indicating a user setting, such as an image resolution setting (e.g., 3840 pixels by 2160 pixels), a frame rate setting (e.g., 60 frames per second (fps)), a location setting, and/or a context setting, which may indicate an activity, such as mountain biking, in response to user input, and may communicate the settings, or related information, to the image capture apparatus  110 . 
       FIG.  2    is a block diagram of an example of an image capture device  200  in accordance with implementations of this disclosure. In some implementations, an image capture device  200 , such as one of the image capture devices  130 ,  132 ,  134  shown in  FIG.  1   , which may be an action camera, may include an audio component  210 , a user interface (UI) unit  212 , an input/output (I/O) unit  214 , a sensor controller  220 , a processor  222 , an electronic storage unit  224 , an image sensor  230 , a metadata unit  232 , an optics unit  234 , a communication unit  240 , a power system  250 , or a combination thereof. 
     In some implementations, the audio component  210 , which may include a microphone, may receive, sample, capture, record, or a combination thereof audio information, such as sound waves, which may be associated with, such as stored in association with, image or video content contemporaneously captured by the image capture device  200 . In some implementations, audio information may be encoded using, e.g., Advanced Audio Coding (AAC), Audio Compression—3 (AC3), Moving Picture Experts Group Layer-3 Audio (MP3), linear Pulse Code Modulation (PCM), Motion Picture Experts Group—High efficiency coding and media delivery in heterogeneous environments (MPEG-H), and/or other audio coding formats (audio codecs). In one or more implementations of spherical video and/or audio, the audio codec may include a three-dimensional audio codec, such as Ambisonics. For example, an Ambisonics codec can produce full surround audio including a height dimension. Using a G-format Ambisonics codec, a special decoder may be omitted. 
     In some implementations, the user interface unit  212  may include one or more units that may register or receive input from and/or present outputs to a user, such as a display, a touch interface, a proximity sensitive interface, a light receiving/emitting unit, a sound receiving/emitting unit, a wired/wireless unit, and/or other units. In some implementations, the user interface unit  212  may include a display, one or more tactile elements (e.g., buttons and/or virtual touch screen buttons), lights (LEDs), speakers, and/or other user interface elements. The user interface unit  212  may receive user input and/or provide information to a user related to the operation of the image capture device  200 . 
     In some implementations, the user interface unit  212  may include a display unit that presents information related to camera control or use, such as operation mode information (e.g., image resolution, frame rate, capture mode, sensor mode, video mode, photo mode), connection status information (e.g., connected, wireless, wired connection), power mode information (e.g., standby mode, sensor mode, video mode), information related to other information sources (e.g., heart rate, GPS), and/or other information. 
     In some implementations, the user interface unit  212  may include a user interface component such as one or more buttons, which may be operated, such as by a user, to control camera operations, such as to start, stop, pause, and/or resume sensor and/or content capture. The camera control associated with respective user interface operations may be defined. For example, the camera control associated with respective user interface operations may be defined based on the duration of a button press (pulse width modulation), a number of button presses (pulse code modulation), or a combination thereof. In an example, a sensor acquisition mode may be initiated in response to detecting two short button presses. In another example, the initiation of a video mode and cessation of a photo mode, or the initiation of a photo mode and cessation of a video mode, may be triggered (toggled) in response to a single short button press. In another example, video or photo capture for a given time duration or a number of frames (burst capture) may be triggered in response to a single short button press. Other user command or communication implementations may also be implemented, such as one or more short or long button presses. 
     In some implementations, the I/O unit  214  may synchronize the image capture device  200  with other cameras and/or with other external devices, such as a remote control, a second image capture device, a smartphone, a user interface device, such as the user interface device  120  shown in  FIG.  1   , and/or a video server. The I/O unit  214  may communicate information between I/O components. In some implementations, the I/O unit  214  may be connected to the communication unit  240  to provide a wired and/or wireless communications interface (e.g., Wi-Fi, Bluetooth, USB, HDMI, Wireless USB, Near Field Communication (NFC), Ethernet, a radio frequency transceiver, and/or other interfaces) for communication with one or more external devices, such as a user interface device, such as the user interface device  120  shown in  FIG.  1   , or another metadata source. In some implementations, the I/O unit  214  may interface with LED lights, a display, a button, a microphone, speakers, and/or other I/O components. In some implementations, the I/O unit  214  may interface with an energy source, e.g., a battery, and/or a Direct Current (DC) electrical source. 
     In some implementations, the I/O unit  214  of the image capture device  200  may include one or more connections to external computerized devices for configuration and/or management of remote devices, as described herein. The I/O unit  214  may include any of the wireless or wireline interfaces described herein, and/or may include customized or proprietary connections for specific applications. 
     In some implementations, the sensor controller  220  may operate or control the image sensor  230 , such as in response to input, such as user input. In some implementations, the sensor controller  220  may receive image and/or video input from the image sensor  230  and may receive audio information from the audio component  210 . 
     In some implementations, the processor  222  may include a system on a chip (SOC), microcontroller, microprocessor, CPU, DSP, application-specific integrated circuit (ASIC), GPU, and/or other processor that may control the operation and functionality of the image capture device  200 . In some implementations, the processor  222  may interface with the sensor controller  220  to obtain and process sensory information for, e.g., object detection, face tracking, stereo vision, and/or other image processing. 
     In some implementations, the sensor controller  220 , the processor  222 , or both may synchronize information received by the image capture device  200 . For example, timing information may be associated with received sensor data, and metadata information may be related to content (photo/video) captured by the image sensor  230  based on the timing information. In some implementations, the metadata capture may be decoupled from video/image capture. For example, metadata may be stored before, after, and in-between the capture, processing, or storage of one or more video clips and/or images. 
     In some implementations, the sensor controller  220 , the processor  222 , or both may evaluate or process received metadata and may generate other metadata information. For example, the sensor controller  220  may integrate the received acceleration information to determine a velocity profile for the image capture device  200  concurrent with recording a video. In some implementations, video information may include multiple frames of pixels and may be encoded using an encoding method (e.g., H. 265 , H. 264 , CineForm, and/or other codec). 
     Although not shown separately in  FIG.  2   , one or more of the audio component  210 , the user interface unit  212 , the I/O unit  214 , the sensor controller  220 , the processor  222 , the electronic storage unit  224 , the image sensor  230 , the metadata unit  232 , the optics unit  234 , the communication unit  240 , or the power systems  250  of the image capture device  200  may communicate information, power, or both with one or more other units, such as via an electronic communication pathway, such as a system bus. For example, the processor  222  may interface with the audio component  210 , the user interface unit  212 , the I/O unit  214 , the sensor controller  220 , the electronic storage unit  224 , the image sensor  230 , the metadata unit  232 , the optics unit  234 , the communication unit  240 , or the power systems  250  via one or more driver interfaces and/or software abstraction layers. In some implementations, one or more of the units shown in  FIG.  2    may include a dedicated processing unit, memory unit, or both (not shown). In some implementations, one or more components may be operable by one or more other control processes. For example, a GPS receiver may include a processing apparatus that may provide position and/or motion information to the processor  222  in accordance with a defined schedule (e.g., values of latitude, longitude, and elevation at 10 Hz). 
     In some implementations, the electronic storage unit  224  may include a system memory module that may store executable computer instructions that, when executed by the processor  222 , perform various functionalities including those described herein. For example, the electronic storage unit  224  may be a non-transitory computer-readable storage medium, which may include executable instructions, and a processor, such as the processor  222  may execute the instruction to perform one or more, or portions of one or more, of the operations described herein. The electronic storage unit  224  may include storage memory for storing content (e.g., metadata, images, audio) captured by the image capture device  200 . 
     In some implementations, the electronic storage unit  224  may include non-transitory memory for storing configuration information and/or processing code for video information and metadata capture, and/or to produce a multimedia stream that may include video information and metadata in accordance with the present disclosure. In some implementations, the configuration information may include capture type (video, still images), image resolution, frame rate, burst setting, white balance, recording configuration (e.g., loop mode), audio track configuration, and/or other parameters that may be associated with audio, video, and/or metadata capture. In some implementations, the electronic storage unit  224  may include memory that may be used by other hardware/firmware/software elements of the image capture device  200 . 
     In some implementations, the image sensor  230  may include one or more of a charge-coupled device sensor, an active pixel sensor, a complementary metal-oxide semiconductor sensor, an N-type metal-oxide-semiconductor sensor, and/or another image sensor or combination of image sensors. In some implementations, the image sensor  230  may be controlled based on control signals from a sensor controller  220 . 
     The image sensor  230  may sense or sample light waves gathered by the optics unit  234  and may produce image data or signals. The image sensor  230  may generate an output signal conveying visual information regarding the objects or other content corresponding to the light waves received by the optics unit  234 . The visual information may include one or more of an image, a video, and/or other visual information. 
     In some implementations, the image sensor  230  may include a video sensor, an acoustic sensor, a capacitive sensor, a radio sensor, a vibrational sensor, an ultrasonic sensor, an infrared sensor, a radar sensor, a Light Detection And Ranging (LIDAR) sensor, a sonar sensor, or any other sensory unit or combination of sensory units capable of detecting or determining information in a computing environment. 
     In some implementations, the metadata unit  232  may include sensors such as an IMU, which may include one or more accelerometers and/or gyroscopes, a magnetometer, a compass, a GPS sensor, an altimeter, an ambient light sensor, a temperature sensor, and/or other sensors or combinations of sensors. In some implementations, the image capture device  200  may contain one or more other metadata/telemetry sources, e.g., image sensor parameters, battery monitor, storage parameters, and/or other information related to camera operation and/or capture of content. The metadata unit  232  may obtain information related to the environment of the image capture device  200  and aspects in which the content is captured. 
     For example, the metadata unit  232  may include an accelerometer that may provide device motion information including velocity and/or acceleration vectors representative of motion of the image capture device  200 . In another example, the metadata unit  232  may include a gyroscope that may provide orientation information describing the orientation of the image capture device  200 . In another example, the metadata unit  232  may include a GPS sensor that may provide GPS coordinates, time, and information identifying a location of the image capture device  200 . In another example, the metadata unit  232  may include an altimeter that may obtain information indicating an altitude of the image capture device  200 . 
     In some implementations, the metadata unit  232 , or one or more portions thereof, may be rigidly coupled to the image capture device  200  such that motion, changes in orientation, or changes in the location of the image capture device  200  may be accurately detected by the metadata unit  232 . Although shown as a single unit, the metadata unit  232 , or one or more portions thereof, may be implemented as multiple distinct units. For example, the metadata unit  232  may include a temperature sensor as a first physical unit and a GPS unit as a second physical unit. In some implementations, the metadata unit  232 , or one or more portions thereof, may be included in an image capture device  200  as shown, or may be included in a physically separate unit operatively coupled to, such as in communication with, the image capture device  200 . 
     In some implementations, the optics unit  234  may include one or more of a lens, macro lens, zoom lens, special-purpose lens, telephoto lens, prime lens, achromatic lens, apochromatic lens, process lens, wide-angle lens, ultra-wide-angle lens, fisheye lens, infrared lens, ultraviolet lens, perspective control lens, other lens, and/or other optics component. In some implementations, the optics unit  234  may include a focus controller unit that may control the operation and configuration of the camera lens. The optics unit  234  may receive light from an object and may focus received light onto an image sensor  230 . Although not shown separately in  FIG.  2   , in some implementations, the optics unit  234  and the image sensor  230  may be combined, such as in a combined physical unit, such as a housing. 
     In some implementations, the communication unit  240  may be coupled to the I/O unit  214  and may include a component (e.g., a dongle) having an infrared sensor, a radio frequency transceiver and antenna, an ultrasonic transducer, and/or other communications interfaces used to send and receive wireless communication signals. In some implementations, the communication unit  240  may include a local (e.g., Bluetooth, Wi-Fi) and/or broad range (e.g., cellular LTE) communications interface for communication between the image capture device  200  and a remote device (e.g., the user interface device  120  in  FIG.  1   ). The communication unit  240  may communicate using, for example, Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 3G, Long Term Evolution (LTE), digital subscriber line (DSL), asynchronous transfer mode (ATM), InfiniBand, PCI Express Advanced Switching, and/or other communication technologies. In some implementations, the communication unit  240  may communicate using networking protocols, such as multiprotocol label switching (MPLS), transmission control protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP), hypertext transport protocol (HTTP), simple mail transfer protocol (SMTP), file transfer protocol (FTP), and/or other networking protocols. 
     Information exchanged via the communication unit  240  may be represented using formats including one or more of hypertext markup language (HTML), extensible markup language (XML), and/or other formats. One or more exchanges of information between the image capture device  200  and remote or external devices may be encrypted using encryption technologies including one or more of secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet Protocol security (IPsec), and/or other encryption technologies. 
     In some implementations, the one or more power systems  250  supply power to the image capture device  200 . For example, for a small-sized, lower-power action camera a wireless power solution (e.g., battery, solar cell, inductive (contactless) power source, rectification, and/or other power supply) may be used. 
     Consistent with the present disclosure, the components of the image capture device  200  may be remote from one another and/or aggregated. For example, one or more sensor components may be distal from the image capture device  200 , e.g., such as shown and described with respect to  FIG.  1   . Multiple mechanical, sensory, or electrical units may be controlled by a learning apparatus via network/radio connectivity. 
       FIG.  3    is a cross-sectional view of an example of a dual-lens image capture apparatus  300  including overlapping fields-of-view  310 ,  312  in accordance with implementations of this disclosure. In some implementations, the image capture apparatus  300  may be a spherical image capture apparatus with fields-of-view  310 ,  312  as shown in  FIG.  3   . For example, the image capture apparatus  300  may include image capture devices  320 ,  322 , related components, or a combination thereof, arranged in a back-to-back or Janus configuration. For example, a first image capture device  320  may include a first lens  330  and a first image sensor  340 , and a second image capture device  322  may include a second lens  332  and a second image sensor  342  arranged oppositely from the first lens  330  and the first image sensor  340 . 
     The first lens  330  of the image capture apparatus  300  may have the field-of-view  310  shown above a boundary  350 . Behind the first lens  330 , the first image sensor  340  may capture a first hyper-hemispherical image plane from light entering the first lens  330 , corresponding to the first field-of-view  310 . 
     The second lens  332  of the image capture apparatus  300  may have a field-of-view  312  as shown below a boundary  352 . Behind the second lens  332 , the second image sensor  342  may capture a second hyper-hemispherical image plane from light entering the second lens  332 , corresponding to the second field-of-view  312 . 
     In some implementations, one or more areas, such as blind spots  360 ,  362 , may be outside of the fields-of-view  310 ,  312  of the lenses  330 ,  332 , light may be obscured from the lenses  330 ,  332  and the respective image sensors  340 ,  342 , and content in the blind spots  360 ,  362  may be omitted from capture. In some implementations, the image capture apparatus  300  may be configured to minimize the blind spots  360 ,  362 . 
     In some implementations, the fields-of-view  310 ,  312  may overlap. Stitch points  370 ,  372 , proximal to the image capture apparatus  300 , at which the fields-of-view  310 ,  312  overlap may be referred to herein as overlap points or stitch points. Content captured by the respective lenses  330 ,  332 , distal to the stitch points  370 ,  372 , may overlap. 
     In some implementations, images contemporaneously captured by the respective image sensors  340 ,  342  may be combined to form a combined image. Combining the respective images may include correlating the overlapping regions captured by the respective image sensors  340 ,  342 , aligning the captured fields-of-view  310 ,  312 , and stitching the images together to form a cohesive combined image. 
     In some implementations, a small change in the alignment (e.g., position and/or tilt) of the lenses  330 ,  332 , the image sensors  340 ,  342 , or both may change the relative positions of their respective fields-of-view  310 ,  312  and the locations of the stitch points  370 ,  372 . A change in alignment may affect the size of the blind spots  360 ,  362 , which may include changing the size of the blind spots  360 ,  362  unequally. 
     In some implementations, incomplete or inaccurate information indicating the alignment of the image capture devices  320 ,  322 , such as the locations of the stitch points  370 ,  372 , may decrease the accuracy, efficiency, or both of generating a combined image. In some implementations, the image capture apparatus  300  may maintain information indicating the location and orientation of the lenses  330 ,  332  and the image sensors  340 ,  342  such that the fields-of-view  310 ,  312 , stitch points  370 ,  372 , or both may be accurately determined, which may improve the accuracy, efficiency, or both of generating a combined image. 
     In some implementations, optical axes through the lenses  330 ,  332  may be substantially antiparallel to each other, such that the respective axes may be within a tolerance such as 1%, 3%, 5%, 10%, and/or other tolerances. In some implementations, the image sensors  340 ,  342  may be substantially perpendicular to the optical axes through their respective lenses  330 ,  332 , such that the image sensors may be perpendicular to the respective axes to within a tolerance such as 1%, 3%, 5%, 10%, and/or other tolerances. 
     In some implementations, the lenses  330 ,  332  may be laterally offset from each other, may be off-center from a central axis of the image capture apparatus  300 , or may be laterally offset and off-center from the central axis. As compared to an image capture apparatus with back-to-back lenses (e.g., lenses aligned along the same axis), the image capture apparatus  300  including laterally offset lenses  330 ,  332  may include substantially reduced thickness relative to the lengths of the lens barrels securing the lenses  330 ,  332 . For example, the overall thickness of the image capture apparatus  300  may be close to the length of a single lens barrel as opposed to twice the length of a single lens barrel as in a back-to-back configuration. Reducing the lateral distance between the lenses  330 ,  332  may improve the overlap in the fields-of-view  310 ,  312 . 
     In some implementations, images or frames captured by an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  shown in  FIG.  3   , may be combined, merged, or stitched together, to produce a combined image, such as a spherical or panoramic image, which may be an equirectangular planar image. In some implementations, generating a combined image may include three-dimensional, or spatiotemporal, noise reduction (3DNR). In some implementations, pixels along the stitching boundary may be matched accurately to minimize boundary discontinuities. 
       FIG.  4    is a block diagram of an example of an image processing and coding pipeline  400  in accordance with implementations of this disclosure. In some implementations, the image processing and coding pipeline  400  may be included in an image capture device, such as the image capture device  200  shown in  FIG.  2   , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  shown in  FIG.  3   . In some implementations, the image processing and coding pipeline  400  may include an image signal processor (ISP)  410 , an encoder  420 , or a combination thereof. 
     In some implementations, the image signal processor  410  may receive an input image signal  430 . For example, an image sensor (not shown), such as image sensor  230  shown in  FIG.  2   , may capture an image, or a portion thereof, and may send, or transmit, the captured image, or image portion, to the image signal processor  410  as the input image signal  430 . In some implementations, an image, or frame, such as an image, or frame, included in the input image signal, may be one of a sequence or series of images or frames of a video, such as a sequence, or series, of frames captured at a rate, or frame rate, which may be a number or cardinality of frames captured per defined temporal period, such as 24, 30, or 60 frames per second. 
     In some implementations, the image signal processor  410  may include a local motion estimation (LME) unit  412 , which may generate local motion estimation information for use in image signal processing and encoding, such as in correcting distortion, stitching, and/or motion compensation. In some implementations, the local motion estimation unit  412  may partition the input image signal  430  into blocks (e.g., having 4×4, 16×16, 64×64, and/or other dimensions). In some implementations, the local motion estimation unit  412  may partition the input image signal  430  into arbitrarily shaped patches and/or individual pixels. 
     In some implementations, the local motion estimation unit  412  may compare pixel values of blocks of pixels between image frames, such as successive image frames, from the input image signal  430  to determine displacement, or movement, between frames. The local motion estimation unit  412  may produce motion vectors (e.g., an x component and y component of motion) at multiple locations within an image frame. The motion vectors may be represented by a translational model or other models that may approximate camera motion, such as rotation and translation in three dimensions, and zooming. 
     In some implementations, the image signal processor  410  of the image processing and coding pipeline  400  may include electronic storage  414 , such as memory (e.g., random access memory (RAM), flash, or other types of memory). The electronic storage  414  may store local motion estimation information  416  determined by the local motion estimation unit  412  for one or more frames. The local motion estimation information  416  and associated image or images may be passed as output  440  to the encoder  420 . In some implementations, the electronic storage  414  may include a buffer, or cache, and may buffer the input image signal as an input, or source, image, or frame. 
     In some implementations, the image signal processor  410  may output an image, associated local motion estimation information  416 , or both as the output  440 . For example, the image signal processor  410  may receive the input image signal  430 , process the input image signal  430 , and output a processed image as the output  440 . Processing the input image signal  430  may include generating and using the local motion estimation information  416 , spatiotemporal noise reduction (3DNR), dynamic range enhancement, local tone adjustment, exposure adjustment, contrast adjustment, image stitching, and/or other operations. 
     The encoder  420  may encode or compress the output  440  of the image signal processor  410 . In some implementations, the encoder  420  may implement the one or more encoding standards, which may include motion estimation. 
     In some implementations, the encoder  420  may output encoded video as an encoded output  450 . For example, the encoder  420  may receive the output  440  of the image signal processor  410 , which may include processed images, the local motion estimation information  416 , or both. The encoder  420  may encode the images and may output the encoded images as the encoded output  450 . 
     In some implementations, the encoder  420  may include a motion estimation unit  422  that may determine motion information for encoding the image of output  440  of the image signal processor  410 . In some implementations, the encoder  420  may encode the image of output  440  of the image signal processor  410  using motion information generated by the motion estimation unit  422  of the encoder  420 , the local motion estimation information  416  generated by the local motion estimation unit  412  of the image signal processor  410 , or a combination thereof. For example, the motion estimation unit  422  may determine motion information at pixel block sizes that may differ from pixel block sizes used by the local motion estimation unit  412 . In another example, the motion estimation unit  422  of the encoder  420  may generate motion information and the encoder may encode the image of output  440  of the image signal processor  410  using the motion information generated by the motion estimation unit  422  of the encoder  420  and the local motion estimation information  416  generated by the local motion estimation unit  412  of the image signal processor  410 . In another example, the motion estimation unit  422  of the encoder  420  may use the local motion estimation information  416  generated by the local motion estimation unit  412  of the image signal processor  410  as input for efficiently and accurately generating motion information. 
     In some implementations, the image signal processor  410 , the encoder  420 , or both may be distinct units, as shown. For example, the image signal processor  410  may include a motion estimation unit, such as the local motion estimation unit  412  as shown, and/or the encoder  420  may include a motion estimation unit, such as the motion estimation unit  422 . 
     In some implementations, the image signal processor  410  may store motion information, such as the local motion estimation information  416 , in a memory, such as the electronic storage  414 , and the encoder  420  may read the motion information from the electronic storage  414  or otherwise receive the motion information from the image signal processor  410 . The encoder  420  may use the motion estimation information determined by the image signal processor  410  for motion compensation processing. 
       FIG.  5    is a functional block diagram of an example of an image signal processor  500  in accordance with implementations of this disclosure. An image signal processor  500  may be included in an image capture device, such as the image capture device  200  shown in  FIG.  2   , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  shown in  FIG.  3   . In some implementations, the image signal processor  500  may be similar to the image signal processor  410  shown in  FIG.  4   . 
     The image signal processor  500  may receive an image signal, such as from an image sensor (not shown), such as the image sensor  230  shown in  FIG.  2   , in a defined format, such as a format of the image sensor, which may be referred to herein as “raw,” such as “a raw image,” “raw image data,” “raw data,” “a raw signal,” or “a raw image signal.” For example, the raw image signal may be in a format such as RGB format, which may represent individual pixels using a combination of values or components, such as a red component (R), a green component (G), and a blue component (B). In some implementations, the image signal processor  500  may convert the raw image data (RGB data) to another format, such as a format expressing individual pixels using a combination of values or components, such as a luminance, or luma, value (Y), a blue chrominance, or chroma, value (U or Cb), and a red chroma value (V or Cr), such as the YUV or YCbCr formats. 
     The image signal processor  500  may include a front image signal processor (Front ISP)  510 , or multiple front image signal processors as shown, a local motion estimation (LME) unit  520 , a local motion compensation (LMC) unit  522 , a global motion compensation (GMC) unit  524 , a high dynamic range (HDR) unit  530 , a three-dimensional noise reduction (3DNR) unit  540 , which may include a temporal noise reduction (TNR) unit  542  and a raw to raw (R2R) unit  544 , a raw to YUV (R2Y) unit  550 , a YUV to YUV (Y2Y) unit  560 , a warp and blend unit  570 , a stitching cost unit  580 , a scaler  585 , an image signal processor bus (ISP BUS)  590 , a configuration controller  595 , or a combination thereof. 
     Although not shown expressly in  FIG.  5   , in some implementations, one or more of the front image signal processor  510 , the local motion estimation unit  520 , the local motion compensation unit  522 , the global motion compensation unit  524 , the high dynamic range unit  530 , the three-dimensional noise reduction unit  540 , the temporal noise reduction unit  542 , the raw to raw unit  544 , the raw to YUV unit  550 , the YUV to YUV unit  560 , the warp and blend unit  570 , the stitching cost unit  580 , the scaler  585 , the image signal processor bus  590 , the configuration controller  595 , or any combination thereof, may include a respective clock, power domain, or both. 
     In some implementations, the front image signal processor  510  may minimally process image signals received from respective image sensors, which may include image scaling. Scaling, by the front image signal processor  510 , may include processing pixels, such as a defined cardinality of pixels, corresponding to a determined quality. For example, the front image signal processor  510  may correct dead pixels, perform band processing, decouple vertical blanking, or a combination thereof. In some implementations, the front image signal processor  510  may output a high resolution frame, one or more downscaled, or reduced, resolution frames, such as a ½×½ resolution frame, a ¼×¼ resolution frame, a ⅛×⅛ resolution frame, a 1/16× 1/16 resolution frame, a 1/32× 1/32 resolution frame, or any combination thereof. 
     In some implementations, a multiple camera apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , may include multiple image capture devices, such as the image capture device  200  shown in  FIG.  2   , and may include a respective front image signal processor  510  associated with each image capture device. 
     The local motion estimation unit  520  may receive a target image (e.g., a target frame of video) and a reference image (e.g., a reference frame of video) and determine motion information (e.g., a set of motion vectors) that may be used to determine a transformation that may be applied to the reference image to better align image portions (e.g., pixels or blocks of pixels) of the reference image with corresponding image portions of the target image. 
     The local motion estimation unit  520  may receive, or otherwise access, a target image, or one or more portions thereof, which may be a current input frame, such as via the image signal processor bus  590 . In some implementations, the local motion estimation unit  520  may receive the target image, at a downscaled, or reduced, resolution. In some implementations, such as implementations implementing high dynamic range image processing, the target image may be a long exposure input frame. 
     The local motion estimation unit  520  may receive, or otherwise access, a reference image, or one or more portions thereof, such as via the image signal processor bus  590 . In some implementations, such as implementations including temporal noise reduction, the reference image may be a recirculated frame that has been generated based on one or more previous frames of video from an image sensor. For example, the reference image may be a recirculated frame from the three-dimensional noise reduction unit  540 . In some implementations, such as implementations including high dynamic range image processing, the reference image may be a short exposure input frame corresponding to the long exposure input frame that will be combined with the long exposure input frame to obtain a high dynamic range frame. 
     In some implementations, the local motion estimation unit  520  may receive, or otherwise access, previously generated motion information, such as previously generated motion vectors for the target image or motion information for a previously processed frame. 
     The local motion estimation unit  520  may determine motion information, such as motion vectors, representing motion between the reference image and the target image, such as motion caused by moving objects in the field-of-view or non-rotational motion, or translation, of the field-of-view. The local motion estimation unit  520  may output the motion information. For example, the local motion estimation unit  520  may output motion vectors to the local motion compensation unit  522 . 
     The local motion compensation unit  522  may receive, or otherwise access, a reference image, or one or more portions thereof, such as via the image signal processor bus  590 . In some implementations, such as implementations implementing temporal noise reduction processing, the reference image may be a recirculated frame (e.g., from the three-dimensional noise reduction unit  540 ). In some implementations, such as implementations implementing high dynamic range image processing, the reference image may be the short exposure input frame, where a corresponding long exposure frame has been used as the target image. In some implementations, such as implementations implementing high dynamic range image processing, the reference image may be a long exposure input frame, where a corresponding short exposure frame has been used as the target image. 
     The local motion compensation unit  522  may receive, or otherwise access, motion information, such as motion vectors, associated with the reference image. For example, the local motion compensation unit  522  may receive the motion vectors from the local motion estimation unit  520 . 
     The local motion compensation unit  522  may generate or obtain a prediction image (e.g., a prediction frame), or a portion thereof, such as a prediction block, which may be a prediction of the target image, or a portion thereof, such as a target block of the target image, based on the reference image, or a portion thereof, and the local motion information. For example, a prediction image may be obtained by applying a transformation, which is based on the local motion information, to the reference image (e.g., a recirculated frame or a short exposure frame). The local motion compensation unit  522  may output a local motion prediction image, or one or more portions thereof, which may be referred to herein as a local motion compensated image (e.g., a local motion compensated frame of video). 
     The global motion compensation unit  524  may receive, or otherwise access, the reference image, or one or more portions thereof, such as via the image signal processor bus  590 . In some implementations, such as implementations implementing temporal noise reduction processing, the reference image may be a recirculated frame (e.g., from the three-dimensional noise reduction unit  540 ). In some implementations, such as implementations implementing high dynamic range image processing, the reference image may be a short exposure input frame, where a corresponding long exposure input frame has been used as the target image. In some implementations, such as implementations implementing high dynamic range image processing, the reference image may be a long exposure input frame, where a corresponding short exposure input frame has been used as the target image. 
     The global motion compensation unit  524  may receive, or otherwise access, global motion information, such as global motion information from a gyroscopic unit of the image capture apparatus, such as a gyroscopic sensor included in the metadata unit  232  shown in  FIG.  2   , corresponding to a time period between capture of the reference image and capture of the target image. The global motion information may indicate a non-translational change in the orientation of the field-of-view relative to the content captured in respective images. For example, the global motion information may indicate a horizontal change of the field-of-view, which may indicate that the corresponding camera panned, or rotated, around a vertical axis. In another example, the global motion information may indicate a vertical change of the field-of-view, which may indicate that the camera tilted or rotated around an axis perpendicular to the lens. In another example, the global motion information may indicate a rotational change of the field-of-view relative to the horizon, which may indicate that the camera rolled or rotated around an axis parallel to the lens. The global motion information may be distinct from motion information, such as translation motion information, indicating a change in the geospatial location of the image capture apparatus, which may include a change associated with changing an elevation of the image capture apparatus. 
     The global motion compensation unit  524  may generate or obtain a prediction image (e.g., a prediction frame of video), or a portion thereof, such as a prediction block, which may be a prediction of the target image, or a portion thereof, such as a target block of the target image, based on the reference image, or a portion thereof, and the global motion information. For example, a prediction image may be obtained by applying a transformation, which is based on the global motion information, to the reference image (e.g., a recirculated frame or a short exposure frame). The global motion compensation unit  524  may output a global motion prediction image, or one or more portions thereof, which may be referred to herein as a global motion compensated image (e.g., a global motion compensated frame of video). 
     The high dynamic range unit  530  may receive, or otherwise access, (e.g., from the front image signal processor  510 ) multiple images of a scene that have been captured with different exposure times. The high dynamic range unit  530  may combine the images captured with different exposure times to obtain a high dynamic range image. For example, the high dynamic range unit  530  may combine two images, a long exposure image and a short exposure image, to obtain a high dynamic range image. For example, image portions (e.g., pixels or blocks of pixels) of the high dynamic range image may be determined based on corresponding image portions the short exposure image where the respective image portions of the long exposure image have saturated pixel values and may otherwise determine image portions of the high dynamic range based on corresponding image portions the long exposure image. In some implementations, motion compensation (e.g., local motion compensation by the local motion compensation unit  522  and/or global motion compensation by the global motion compensation unit  524 ) may be applied to either the long exposure image or the short exposure image to better align pixels corresponding to objects appearing in the field of view of the two input images. For example, the high dynamic range unit  530  may combine a long exposure image with a motion compensated short exposure image. For example, the high dynamic range unit  530  may combine a short exposure image with a motion compensated long exposure image. The high dynamic range unit  530  may receive, or otherwise access, the local motion prediction image, or a portion thereof, from the local motion compensation unit  522 . The high dynamic range unit  530  may receive, or otherwise access, the global motion prediction image, or a portion thereof, from the global motion compensation unit  524 . 
     The high dynamic range unit  530  may output the high dynamic range image. For example, the high dynamic range unit  530  may output the high dynamic range image by storing the high dynamic range image in memory, such as shared memory, via the image signal processor bus  590 , or the high dynamic range unit  530  may output the high dynamic range image directly to another unit of the image signal processor  500 , such as the temporal noise reduction unit  542 . 
     In some implementations, the high dynamic range unit  530  may be omitted, or high dynamic range processing by the high dynamic range unit  530  may be omitted. 
     The three-dimensional noise reduction unit  540  may include the temporal noise reduction (TNR) unit  542 , the raw to raw (R2R) unit  544 , or both. 
     The temporal noise reduction unit  542  may receive the current input frame, or one or more portions thereof, such as from the front image signal processor  510  or via the image signal processor bus  590 . In some implementations, such as implementations implementing high dynamic range image processing, the temporal noise reduction unit  542  may receive the high dynamic range input frame, or one or more portions thereof, such as from the high dynamic range unit  530 , as the current input frame. 
     The temporal noise reduction unit  542  may receive, or otherwise access, a local motion prediction frame from the local motion compensation unit  522 . The temporal noise reduction unit  542  may receive, or otherwise access, the global motion prediction frame from the global motion compensation unit  524 . 
     The temporal noise reduction unit  542  may reduce temporal noise in the current input frame, which may include recursively reducing temporal noise in a sequence of input images, such as a video. Recursive temporal noise reduction may include combining a current image from a sequence of images (e.g., a current frame from a video) with a recirculated image that is based on one or more previous images from the sequence of images to obtain a noise reduced image. Details of this combination (e.g., mixing weights for respective image portions) may be determined based on noise level information (e.g., a noise map) for the recirculated image. 
     The temporal noise reduction unit  542  may generate output including a pixel value and associated noise variance for the pixel value for one or more pixels of the noise reduced image (e.g., the noise reduced frame). 
     The raw to raw unit  544  may perform spatial denoising of frames of raw images based on noise variance values received from the temporal noise reduction unit  542 . For example, spatial denoising in the raw to raw unit  544  may include multiple passes of image signal processing, including passes at various resolutions. 
     The raw to YUV unit  550  may demosaic, and/or color process, the frames of raw images, which may include representing each pixel in the YUV format, which may include a combination of a luminance (Y) component and two chrominance (UV) components. 
     The YUV to YUV unit  560  may perform local tone mapping of YUV images. In some implementations, the YUV to YUV unit  560  may include multi-scale local tone mapping using a single pass approach or a multi-pass approach on a frame at different scales. 
     The warp and blend unit  570  may warp images, blend images, or both. In some implementations, the warp and blend unit  570  may warp a corona around the equator of each frame to a rectangle. For example, the warp and blend unit  570  may warp a corona around the equator of each frame to a rectangle based on the corresponding low resolution frame generated by the front image signal processor  510 . 
     The warp and blend unit  570  may apply one or more transformations to the frames. In some implementations, spherical images produced by a multi-face camera device, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  shown in  FIG.  3   , may be warped and/or blended by the warp and blend unit  570  to correct for distortions at image edges. In some implementations, the warp and blend unit  570  may apply a transformation that is subject to a close to identity constraint, wherein a location of a pixel in an input image to the warp and blend unit  570  may be similar to, such as within a defined distance threshold of, a location of a corresponding pixel in an output image from the warp and blend unit  570 . For example, the warp and blend unit  570  may include an internal memory, which may have a size, such as 100 lines, which may be smaller than a size of a frame, and the warp and blend unit  570  may process the input image data in raster-in/raster-out order using a transformation that is subject to a close to identity constraint. 
     In some implementations, the warp and blend unit  570  may apply a transformation that is independent of close to identity constraints, which may include processing the input image data in raster-in/dynamic-out or dynamic-in/raster-out order. For example, the warp and blend unit  570  may transform two or more non-rectilinear (fisheye) images to generate a combined frame, such as an equirectangular frame, by processing the input image data in raster-in/dynamic-out or dynamic-in/raster-out order. 
     The stitching cost unit  580  may generate a stitching cost map as an output. In some implementations, the cost map may be represented as a rectangle having disparity x and longitude y based on a warping. Each value of the cost map may be a cost function of a disparity x value for a corresponding longitude. Cost maps may be generated for various scales, longitudes, and disparities. 
     The scaler  585  may scale images received from the output of the warp and blend unit  570 , which may be in patches, or blocks, of pixels, such as 16×16 blocks, 8×8 blocks, or patches or blocks of any other size or combination of sizes. 
     The image signal processor bus  590  may be a bus or interconnect, such as an on-chip interconnect or embedded microcontroller bus interface, for communication between the front image signal processor  510 , the temporal noise reduction unit  542 , the local motion compensation unit  522 , the raw to raw unit  544 , the raw to YUV unit  550 , the YUV to YUV unit  560 , the combined warp and blend unit  570 , the stitching cost unit  580 , the scaler  585 , the configuration controller  595 , or any combination thereof. 
     The configuration controller  595  may coordinate image processing by the front image signal processor  510 , the local motion estimation unit  520 , the local motion compensation unit  522 , the global motion compensation unit  524 , the high dynamic range unit  530 , the three-dimensional noise reduction unit  540 , the temporal noise reduction unit  542 , the raw to raw unit  544 , the raw to YUV unit  550 , the YUV to YUV unit  560 , the warp and blend unit  570 , the stitching cost unit  580 , the scaler  585 , the image signal processor bus  590 , or any combination thereof, of the image signal processor  500 . For example, the configuration controller  595  may control camera alignment model calibration, auto-exposure, auto-white balance, or any other camera calibration or similar process or combination of processes. In some implementations, the configuration controller  595  may be a microcontroller. The configuration controller  595  is shown in  FIG.  5    using broken lines to indicate that the configuration controller  595  may be included in the image signal processor  500  or may be external to, and in communication with, the image signal processor  500 . The configuration controller  595  may include a respective clock, power domain, or both. 
       FIG.  6 A  is a block diagram of an example of a system  600  configured for image capture and stitching. The system  600  includes an image capture device  610  (e.g., a camera or a drone) that includes a processing apparatus  612  that is configured to receive a first image from a first image sensor  614  and receive a second image from a second image sensor  616 . The processing apparatus  612  may be configured to perform image signal processing (e.g., filtering, stitching, and/or encoding) to generated composite images based on image data from the image sensors  614  and  616 . The image capture device  610  includes a communications interface  618  for transferring images to other devices. The image capture device  610  includes a user interface  620 , which may allow a user to control image capture functions and/or view images. The image capture device  610  includes a battery  622  for powering the image capture device  610 . The components of the image capture device  610  may communicate with each other via a bus  624 . The system  600  may be used to implement techniques described in this disclosure, such as the technique  900  of  FIG.  9    and/or the technique  1100  of  FIG.  11   . 
     The processing apparatus  612  may include one or more processors having single or multiple processing cores. The processing apparatus  612  may include memory, such as random access memory device (RAM), flash memory, or any other suitable type of storage device such as a non-transitory computer readable memory. The memory of the processing apparatus  612  may include executable instructions and data that can be accessed by one or more processors of the processing apparatus  612 . For example, the processing apparatus  612  may include one or more DRAM modules such as double data rate synchronous dynamic random-access memory (DDR SDRAM). In some implementations, the processing apparatus  612  may include a digital signal processor (DSP). In some implementations, the processing apparatus  612  may include an application specific integrated circuit (ASIC). For example, the processing apparatus  612  may include a custom image signal processor. 
     The first image sensor  614  and the second image sensor  616  are configured to detect light of a certain spectrum (e.g., the visible spectrum or the infrared spectrum) and convey information constituting an image as electrical signals (e.g., analog or digital signals). For example, the image sensors  614  and  616  may include charge-coupled devices (CCD) or active pixel sensors in complementary metal-oxide-semiconductor (CMOS). The image sensors  614  and  616  may detect light incident through respective lens (e.g., a fisheye lens). In some implementations, the image sensors  614  and  616  include digital to analog converters. In some implementations, the image sensors  614  and  616  are held in a fixed orientation with respective fields of view that overlap. For example, the image sensors  614  and  616  may be configured to capture image data using a plurality of selectable exposure times. 
     The image capture device  610  may include the communications interface  618 , which may enable communications with a personal computing device (e.g., a smartphone, a tablet, a laptop computer, or a desktop computer). For example, the communications interface  618  may be used to receive commands controlling image capture and processing in the image capture device  610 . For example, the communications interface  618  may be used to transfer image data to a personal computing device. For example, the communications interface  618  may include a wired interface, such as a high-definition multimedia interface (HDMI), a universal serial bus (USB) interface, or a FireWire interface. For example, the communications interface  618  may include a wireless interface, such as a Bluetooth interface, a ZigBee interface, and/or a Wi-Fi interface. 
     The image capture device  610  may include the user interface  620 . For example, the user interface  620  may include an LCD display for presenting images and/or messages to a user. For example, the user interface  620  may include a button or switch enabling a person to manually turn the image capture device  610  on and off. For example, the user interface  620  may include a shutter button for snapping pictures. 
     The image capture device  610  may include the battery  622  that powers the image capture device  610  and/or its peripherals. For example, the battery  622  may be charged wirelessly or through a micro-USB interface. 
       FIG.  6 B  is a block diagram of an example of a system  630  configured for image capture and stitching. The system  630  includes an image capture device  640  that communicates via a communications link  650  with a personal computing device  660 . The image capture device  640  includes a first image sensor  642  and a second image sensor  644  that are configured to capture respective images. The image capture device  640  includes a communications interface  646  configured to transfer images via the communication link  650  to the personal computing device  660 . The personal computing device  660  includes a processing apparatus  662 , a user interface  664 , and a communications interface  666 . The processing apparatus  662  is configured to receive, using the communications interface  666 , a first image from the first image sensor  642 , and receive a second image from the second image sensor  644 . The processing apparatus  662  may be configured to perform image signal processing (e.g., filtering, stitching, and/or encoding) to generated composite images based on image data from the image sensors  642  and  644 . The system  630  may be used to implement techniques described in this disclosure, such as the technique  900  of  FIG.  9    and/or the technique  1100  of  FIG.  11   . 
     The first image sensor  642  and the second image sensor  644  are configured to detect light of a certain spectrum (e.g., the visible spectrum or the infrared spectrum) and convey information constituting an image as electrical signals (e.g., analog or digital signals). For example, the image sensors  642  and  644  may include charge-coupled devices (CCD) or active pixel sensors in complementary metal-oxide-semiconductor (CMOS). The image sensors  642  and  644  may detect light incident through respective lens (e.g., a fisheye lens). In some implementations, the image sensors  642  and  644  include digital to analog converters. In some implementations, the image sensors  642  and  644  are held in a fixed relative orientation with respective fields of view that overlap. For example, the image sensors  642  and  644  may be configured to capture image data using a plurality of selectable exposure times. Image signals from the image sensors  642  and  644  may be passed to other components of the image capture device  640  via a bus  648 . 
     The communications link  650  may be wired communications link or a wireless communications link. The communications interface  646  and the communications interface  666  may enable communications over the communications link  650 . For example, the communications interface  646  and the communications interface  666  may include a high-definition multimedia interface (HDMI), a universal serial bus (USB) interface, a FireWire interface, a Bluetooth interface, a ZigBee interface, and/or a Wi-Fi interface. For example, the communications interface  646  and the communications interface  666  may be used to transfer image data from the image capture device  640  to the personal computing device  660  for image signal processing (e.g., filtering, stitching, and/or encoding) to generated composite images based on image data from the image sensors  642  and  644 . 
     The processing apparatus  662  may include one or more processors having single or multiple processing cores. The processing apparatus  662  may include memory, such as random access memory device (RAM), flash memory, or any other suitable type of storage device such as a non-transitory computer readable memory. The memory of the processing apparatus  662  may include executable instructions and data that can be accessed by one or more processors of the processing apparatus  662 . For example, the processing apparatus  662  may include one or more DRAM modules such as double data rate synchronous dynamic random-access memory (DDR SDRAM). In some implementations, the processing apparatus  662  may include a digital signal processor (DSP). In some implementations, the processing apparatus  662  may include an application specific integrated circuit (ASIC). For example, the processing apparatus  662  may include a custom image signal processor. The processing apparatus  662  may exchange data (e.g., image data) with other components of the personal computing device  660  via the bus  668 . 
     The personal computing device  660  may include the user interface  664 . For example, the user interface  664  may include a touchscreen display for presenting images and/or messages to a user and receiving commands from a user. For example, the user interface  664  may include a button or switch enabling a person to manually turn the personal computing device  660  on and off. In some implementations, commands (e.g., start recording video, stop recording video, or snap photograph) received via the user interface  664  may be passed on to the image capture device  640  via the communications link  650 . 
       FIG.  7    is a block diagram of an example of an image processing pipeline  700  for capturing images and reducing noise in the images. The image processing pipeline  700  includes an image sensor  710  configured to capture images (e.g., frames of video); a front ISP  712  configured for initial processing of captured images; a three-dimensional noise reduction module  720 , which includes a temporal noise reduction module  722  and a spatial noise reduction module  724 , that combines corresponding nearby pixels in space and time (e.g., within a two dimensional image and between images in a sequence of images) to reduce noise in the pixel values; a motion compensation module  730 , which includes a local motion compensation module  732  and a global motion compensation module  734 , that may apply transformations to recirculated images from the three-dimensional noise reduction module  720  to better align pixels in a recirculated images with corresponding pixels in a current image from the front ISP  712  to improve pixel hit rates for noise reduction operations applied in the three-dimensional noise reduction module  720 ; and modules  790  for additional processing and outputting images based on the noise reduced images  752  from the three-dimensional noise reduction module  720 . For example, the image processing pipeline  700  may be included in the image capture device  610  of  FIG.  6 A . For example, the image processing pipeline  700  may be included in the system  630  of  FIG.  6 B . In some implementations, the image processing pipeline  700  may be included in an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  shown in  FIG.  3   . 
     The image processing pipeline  700  includes the image sensor  710 . The input image signal  740  from the image sensor  710  is passed to the front ISP  712  for initial processing. For example, the front ISP  712  may be similar to front image signal processor  510  of  FIG.  5    and implement some or all of that component&#39;s functions. The front image signal processor  712  may process the input image signal  740  to generate a current image  742  in a sequence of images (e.g., a current frame from a video) captured using the image sensor  710 . In some implementations, the front ISP  712  may determine one or more low resolution images based on the current image  742 . The low resolution image(s) (not shown) may be output along with the current image  742  and passed to other modules (e.g., the motion compensation module  730 ) that may use the low resolution copies of the current image  742 . Having a low resolution image included along with the current image  742  may facilitate efficient performance of downstream functions in the image processing pipeline  700 . 
     The three-dimensional noise reduction module  720  is configured to combine the current image  742  (e.g., a current frame of a captured video) with a recirculated image  754  (e.g., a recirculated frame of video) to obtain a noise reduced image  752  (e.g., a noise reduced frame of video), where the recirculated image  754  is based on one or more previous images of a sequence of images (e.g., previous frames of video) from the image sensor  710 . The three-dimensional noise reduction module  720  includes a temporal noise reduction module  722  that recursively combines the current image with the recirculated image to obtain a temporal noise reduced image  750 . The temporal noise reduction module  722  may combine the current image  742  with the recirculated image  754  using a set of mixing weights for respective image portions (e.g., pixels or blocks of pixels) of the recirculated image  754 . For example, an image portion of the temporal noise reduced image  750  may be determined as the weighted sum:
 
NR_ n=w _ n *Rn+(1− w _ n )*C_n  [Equation 1]
 
where NR_n is an nth image portion of the temporal noise reduced image  750 , w_n is a mixing weight for an nth image portion of the recirculated image  754 , R_n is the nth image portion of the recirculated image  754 , and C_n is the nth image portion of the current image  742 . The mixing weights for a recirculated image  754  may be determined based on a noise map  764  for the recirculated image  754 . The noise map  764  may include estimates of noise levels (e.g., a variance or a standard deviation) for respective image portions (e.g., pixels of blocks of pixels) of the recirculated image  754 . The mixing weights may also be determined based on estimates of the noise levels for pixels in the current image  742 . For example, the estimates of the noise levels for the current image may be based on a noise model for elements of the image sensor  710 . For example, the estimates of the noise levels for the current image may depend on an exposure time used by the image sensor  710  to capture the current image  742 . In some implementations, a noise model varies with the color channel (e.g., estimates of noise level for red pixels are set to a first value, estimates of noise level for green pixels are set to a second value, and estimates of noise level for blue pixels are set to a third value).
 
     In some implementations, the input signal may be scaled such that the noise level for pixels in the current image is chosen to be a consistent level (e.g., one). For example, the mixing weights may be determined using a Guassian model as:
 
 w _ n=a /(1+ a )  [Equation 1.1]
 
 a =exp(−(( R _ n - C _ n )/(1+SR_ n )){circumflex over ( )}2)/SR_ n {circumflex over ( )}2  [Equation 1.2]
 
where w_n is a mixing weight for an nth image portion of the recirculated image  754 , R_n is the nth image portion of the recirculated image  754 , C_n is the nth image portion of the current image  742 , and SR_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of the recirculated image  754  (e.g., from the noise map  764 ).
 
     The temporal noise reduction module  722  may also determine a noise map  760  for the temporal noise reduced image  750 . In some implementations, the noise map  760  may be determined based on the estimates of noise level for pixels in the current image  742 , the noise map  764  for the recirculated image  754 , and the set of mixing weights used to determine the temporal noise reduced image  750 . For example, a portion of the noise map  760  may be determined as:
 
SNR_ n =sqrt( w _ n {circumflex over ( )}2*SR_ n {circumflex over ( )}2+(1− w _ n ){circumflex over ( )}2*SC_ n {circumflex over ( )}2)  [Equation 2]
 
where SNR_n is an estimate of noise level (e.g., a standard deviation) for an nth image portion of the temporal noise reduced image  750 , w_n is a mixing weight for an nth image portion of the recirculated image  754 , SR_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of the recirculated image  754  (e.g., from the noise map  764 ), and SC_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of the current image  742 .
 
     The spatial noise reduction module  724  may apply spatial noise reduction filtering to the temporal noise reduced image  750  in order to obtain the noise reduced image  752 . The spatial noise reduction module  724  may also determine a noise map  762  for the noise reduced image  752  based on the noise map  760  and a filter applied to the temporal noise reduced image  750  by the spatial noise reduction module  724 . 
     The noise reduced image  752  may be recirculated through the motion compensation module  730 , which may apply a motion compensation transformation to the noise reduced image  752  to obtain a next recirculated image  754 . A motion compensation transformation may be applied to better align pixels of the next recirculated image  754  with corresponding pixels of a next current image  742  to be input to the three-dimensional noise reduction module  720 . The local motion compensation module  732  may apply a local motion compensation transformation to obtain a first candidate recirculated image. The global motion compensation module  734  may apply a global motion compensation transformation to obtain a second candidate recirculated image. In some implementations, quality metrics for the candidate recirculated images may be determined and compared to select a candidate recirculated image as the next recirculated image  754 . A motion compensation transformation (e.g., the local motion compensation transformation, the global motion compensation transformation, or an identity transformation) used to generate the next recirculated image  754  may be used to determine the next noise map  764  for the next recirculated image  754  based on the noise map  762  for the noise reduced image  752 . For example, the motion compensation transformation may be applied to the noise map  762  to obtain the noise map  764 . 
     The noise reduced image  752  is also passed to the modules  790  that may implement addition image processing and output an image based on the noise reduced image  752 . For example, the modules  790  may implement a demosaicing operation to map from a raw format to a YUV domain format (e.g., as described in relation to the raw to YUV unit  550 ); a tone mapping operation (e.g., as described in relation to the YUV to YUV unit  560 ), which may include a local tone mapping and/or a global tone mapping; a warp transformation (e.g., as described in relation to the warp and blend unit  570 ), which may correct distortions such as lens distortion and electronic rolling shutter distortion and/or stitch images from the image sensor  710  with images from one or more other image sensors of an image capture apparatus (e.g., the image capture apparatus  110  or the image capture apparatus  300 ); and/or an encoding operation to compress and encode an image (e.g., a frame of a video) based on the noise reduced image  752 . 
     In some implementations (not shown), the three-dimensional noise reduction module  720  may apply spatial noise reduction to current images  742  from the image sensor  710  before applying recursive temporal noise reduction based on recirculated images  754  to obtain the noise reduced images  752 , i.e., the order of spatial and temporal noise reduction processing may be reversed. In this case, noise estimates for pixels of the current images may be updated based on a filter applied for spatial noise reduction to determine a noise map for a resulting spatial noise reduced image. The noise map  762  for the noise reduced image  752  may then be determined based on the noise map for the resulting spatial noise reduced image, the noise map  764 , and a set of mixing weights used to combine a recirculated image  754  with the spatial noise reduced image. 
       FIG.  8    is a block diagram of an example of an image processing pipeline  800  for capturing images with high dynamic range and reducing noise in the high dynamic range images. The image processing pipeline  800  includes an image sensor  810  configured to capture images (e.g., frames of video); a front ISP  812  configured for initial processing of captured images; a high dynamic range module  814  that combines images captured with different exposure times by the image sensor  810  to obtain an image with a higher dynamic range than the constituent images; a three-dimensional noise reduction module  820 , which includes a temporal noise reduction module  822  and a spatial noise reduction module  824 , that combines corresponding nearby pixels in space and time (e.g., within a two dimensional image and between images in a sequence of images) to reduce noise in the pixel values; a motion compensation module  830 , which includes a local motion compensation module  832  and a global motion compensation module  834 , that may apply transformations to recirculated images from the three-dimensional noise reduction module  820  to better align pixels in a recirculated images with corresponding pixels in a current image from the front ISP  812  to improve pixel hit rates for noise reduction operations applied in the three-dimensional noise reduction module  820 ; and modules  890  for additional processing and outputting images based on the noise reduced images  852  from the three-dimensional noise reduction module  820 . For example, the image processing pipeline  800  may be included in the image capture device  610  of  FIG.  6 A . For example, the image processing pipeline  800  may be included in the system  630  of  FIG.  6 B . In some implementations, the image processing pipeline  800  may be included in an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  shown in  FIG.  3   . 
     The image processing pipeline  800  includes the image sensor  810 . The input image signal  840  from the image sensor  810  is passed to the front ISP  812  for initial processing. For example, the front ISP  812  may be similar to front ISP  510  of  FIG.  5    and implement some or all of that component&#39;s functions. The front ISP  812  may process the input image signal  840  to generate partially processed images  842  in a sequence of images (e.g., a frames from a video) captured using the image sensor  810  using multiple (e.g., two) different exposure times. For example, the image sensor  810  may be configured to capture image data using a plurality of selectable exposure times. For example the images in a sequence of images captured by the image sensor  810  may alternate between using a short exposure time and a long exposure time. 
     Partially processed images  842  captured with different exposure times may be combined in the high dynamic range module  814  to obtain a high dynamic range images  844 . For example, image portions (e.g., pixels or blocks of pixels) of a high dynamic range image  844  may be determined based on a corresponding image portion of a constituent partially processed image  842  with the longest available exposure time that does not have a pixel value that is saturated. The high dynamic range images  844  may include image portions captured with multiple different exposure times. Since noise levels for pixels can depend on exposure time, the high dynamic range images  844  may have different estimates of noise levels in different image portions that vary dynamically between successive high dynamic range images  844  based on the brightness patterns in a captured scene. The high dynamic range module  814  may also determine a noise map  866  for a high dynamic range image  844 , where the noise map  866  is determined based on the particular combination of image components from the constituent partially processed images  842  used to determine the high dynamic range image  844  and estimates of noise level (e.g., depending on the different exposure times and/or color channels) for pixels of those constituent partially processed images  842 . For example, operations of the technique  1100  of  FIG.  11    may be implemented by the high dynamic range module  814  to determine  1120  the high dynamic range image  844  and to determine  1124  a corresponding noise map  866 . 
     The three-dimensional noise reduction module  820  is configured to combine the high dynamic range image  844  (e.g., a current frame of a captured video) with a recirculated image  854  (e.g., a recirculated frame of video) to obtain a noise reduced image  852  (e.g., a noise reduced frame of video), where the recirculated image  854  is based on one or more previous images of a sequence of images (e.g., previous frames of video) from the image sensor  810 . The three-dimensional noise reduction module  820  includes a temporal noise reduction module  822  that recursively combines the high dynamic range image  844  with the recirculated image to obtain a temporal noise reduced image  850 . The temporal noise reduction module  822  may combine the high dynamic range image  844  with the recirculated image  854  using a set of mixing weights for respective image portions (e.g., pixels or blocks of pixels) of the recirculated image  854 . For example, an image portion of the temporal noise reduced image  850  may be determined as the weighted sum:
 
NR_ n=w _ n*R _ n +(1 −w _ n )*HDR_n  [Equation 3]
 
where NR_n is an nth image portion of the temporal noise reduced image  850 , w_n is a mixing weight for an nth image portion of the recirculated image  854 , R_n is the nth image portion of the recirculated image  854 , and HDR_n is the nth image portion of the high dynamic range image  844 . The mixing weights for a recirculated image  854  may be determined based on a noise map  864  for the recirculated image  854 . The noise map  864  may include estimates of noise levels (e.g., a variance or a standard deviation) for respective image portions (e.g., pixels of blocks of pixels) of the recirculated image  854 . The mixing weights may also be determined based on the noise map  866  for the high dynamic range image  844 .
 
     The temporal noise reduction module  822  may also determine a noise map  860  for the temporal noise reduced image  850 . In some implementations, the noise map  860  may be determined based on the noise map  866  for the high dynamic range image  844 , the noise map  864  for the recirculated image  854 , and the set of mixing weights used to determine the temporal noise reduced image  850 . For example, a portion of the noise map  860  may be determined as:
 
SNR_ n =sqrt( w _ n {circumflex over ( )}2*SR_ n {circumflex over ( )}2+(1 −w _ n ){circumflex over ( )}2*SHDR_ n {circumflex over ( )}2)  [Equation 4]
 
where SNR_n is an estimate of noise level (e.g., a standard deviation) for an nth image portion of the temporal noise reduced image  850 , w_n is a mixing weight for an nth image portion of the recirculated image  854 , SR_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of the recirculated image  854  (e.g., from the noise map  864 ), and SHDR_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of the high dynamic range image  844 .
 
     The spatial noise reduction module  824  may apply spatial noise reduction filtering to the temporal noise reduced image  850  in order to obtain the noise reduced image  852 . The noise reduced image  852  may be determined based on the noise map  860 . For example, filter coefficients or mixing weights for combining image pixels of the noise reduced image  850  to obtain pixels of the noise reduced image  852  may be determined based on the noise map  860 . The spatial noise reduction module  824  may also determine a noise map  862  for the noise reduced image  852  based on the noise map  860  and a filter applied to the temporal noise reduced image  850  by the spatial noise reduction module  824 . 
     The noise reduced image  852  may be recirculated through the motion compensation module  830 , which may apply a motion compensation transformation to the noise reduced image  852  to obtain a next recirculated image  854 . A motion compensation transformation may be applied to better align pixels of a recirculated image  854  with corresponding pixels of a next high dynamic range image  844  to be input to the three-dimensional noise reduction module  820 . The local motion compensation module  832  may apply a local motion compensation transformation to obtain a first candidate recirculated image. The global motion compensation module  834  may apply a global motion compensation transformation to obtain a second candidate recirculated image. In some implementations, quality metrics for the candidate recirculated images may be determined and compared to select a candidate recirculated image as the next recirculated image  854 . A motion compensation transformation (e.g., the local motion compensation transformation, the global motion compensation transformation, or an identity transformation) used to generate the next recirculated image  854  may be used to determine the next noise map  864  for the next recirculated image  854  based on the noise map  862  for the noise reduced image  852 . For example, the motion compensation transformation may be applied to the noise map  862  to obtain the noise map  864 . 
     The noise reduced image  852  is also passed to the modules  890  that may implement addition image processing and output an image based on the noise reduced image  852 . For example, the modules  890  may implement a demosaicing operation to map from a raw format to a YUV domain format (e.g., as described in relation to the raw to YUV unit  550 ); a tone mapping operation (e.g., as described in relation to the YUV to YUV unit  560 ), which may include a local tone mapping and/or a global tone mapping; a warp transformation (e.g., as described in relation to the warp and blend unit  570 ), which may correct distortions such as lens distortion and electronic rolling shutter distortion and/or stitch images from the image sensor  810  with images from one or more other image sensors of an image capture apparatus (e.g., the image capture apparatus  110  or the image capture apparatus  300 ); and/or an encoding operation to compress and encode an image (e.g., a frame of a video) based on the noise reduced image  852 . 
       FIG.  9    is a flowchart of an example of a technique  900  for applying three-dimensional noise reduction to captured images. The technique  900  includes receiving  902  a current image from an image sensor; determining  910  mixing weights for recursively combining the current image with a recirculated image; combining  920  the current image with the recirculated image to obtain a noise reduced image; applying  924  spatial noise reduction processing to the noise reduced image; determining  930  a noise map for the noise reduced image; recirculating  940  the noise map with the noise reduced image; and storing, displaying, or transmitting an output image based on the noise reduced image. For example, the technique  900  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  900  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  900  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  900  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ). 
     The technique  900  includes receiving  902  a current image of a sequence of images (e.g., a current frame of video) from an image sensor. The image sensor may be part of an image capture apparatus (e.g., the image capture apparatus  110 , the image capture apparatus  300 , the image capture device  610 , or the image capture device  640 ). For example, the current image may be received  902  from the image sensor via a bus (e.g., the bus  624  or image signal processor bus  590 ). In some implementations, the current image may be received  902  via a communications link (e.g., the communications link  650 ). For example, the current image may be received  902  via a wireless or wired communications interface (e.g., Wi-Fi, Bluetooth, USB, HDMI, Wireless USB, Near Field Communication (NFC), Ethernet, a radio frequency transceiver, and/or other interfaces). For example, the current image may be received  902  via communications interface  666 . For example, the current image may be received  902  via a front ISP (e.g., the front ISP  712  or the front ISP  812 ) that performs some initial processing on the received image. For example, the current image may represent each pixel value in a defined format, such as in a RAW image signal format. For example, the current image be stored in a format using the Bayer color mosaic pattern. In some implementations, the current image may be a current frame of video. 
     In some implementations, the current image may be a high dynamic range image that is received  902  as multiple constituent images and that is determined based on the multiple constituent images, which have been captured by the image sensor with different exposure times. For example, the current image may be determined by combining two images captured by the image sensor using different exposure times, such that the current image has a larger dynamic range than the two images. An input noise map for the current image may be determined based on noise level estimates for both of the two images. The input noise map may specify noise level estimates for respective image portions of the current image. For example, the current image and the input noise map may be determined using operations similar to those described in relation to the technique  1100  of  FIG.  11   . For example, the input noise map may be used by a temporal noise reduction module (e.g., the temporal noise reduction module  822 ) to determine (e.g., by reading from appropriate portions of the input noise map) the estimates of noise levels for pixels in the current image that are used to determine  930  a noise map for a noise reduced image determined (at operation  920 ) based on the current image. 
     The technique  900  includes determining  910  a set of mixing weights for combining the current image with a recirculated image that is based on one or more previous images in the sequence of images from the image sensor. The mixing weights for respective image components (e.g., pixels or blocks of pixels) of the recirculated image may be determined  910  based on estimates of noise levels for those image components and estimates of noise levels for corresponding image components of the current image. For example, estimates of noise levels (e.g., standard deviations or variances) for the recirculated image may be stored in a noise map for the recirculated image. For example, the noise map may be stored at the full resolution of the recirculated image (e.g., one estimate of noise level per pixel) or it may be stored at reduced resolution (e.g., one estimate of noise level per block of pixels). For example, the set of mixing weights may be determined  910  based on the noise map. For example, the mixing weight for a respective image portion may be determined  910  based on a ratio of an estimate of noise level (e.g., from a noise map) for the image portion of the recirculated image to an estimate of noise level (e.g., from a noise map) for a corresponding image portion of the current image. For example, the mixing weight for a respective image portion may be determined  910  as inversely proportional to an estimate of noise level (e.g., from a noise map) for the image portion of the recirculated image. For example, a mixing weight for an image component of the recirculated image may be determined  910  using the technique  1000  of  FIG.  10   . In some implementations, the mixing weight for a respective image portion may be determined  910  using Equation 1.1 and Equation 1.2 above. 
     The technique  900  includes combining  920  the current image (e.g. a current frame of video) with a recirculated image (e.g. a recirculated frame of video) to obtain a noise reduced image (e.g. a noise reduced frame of video). The recirculated image may be based on one or more previous images of the sequence of images from the image sensor. The current image may be combined  920  with the recirculated image using the set of mixing weights for respective image portions of the recirculated image. For example, current image may be combined  920  with recirculated image using the Equation 1 or the Equation 3 above. 
     The technique  900  includes applying  924  spatial noise reduction processing to the noise reduced frame after combining the current frame with the recirculated frame. Applying  924  spatial noise reduction processing may include applying a filter function to the current image. For example, applying  924  spatial noise reduction processing may include averaging nearby (e.g., for pixels within an 8 pixel radius or an 8×8 block of pixels) pixel values to determine a new value for a pixel at the center or the averaged area. In some implementations, pixels with pixel values that differ from the pixel value for the pixel being adjusted by more than a threshold amount are ignored and not included in the average used to the determine the new value of the pixel. In some implementations, estimates of noise level from a noise map for the noise reduced frame (e.g., the noise map  860 ) are used to determine a level of similarity between nearby pixel values. For example, the respective thresholds for determining whether pixels are similar and will be combined during spatial noise reduction processing may be determined based on respective noise level estimates corresponding to the pixels. A variety of spatial filter functions or kernels (e.g., a guassian kernel) may be used for spatial noise reduction processing. Averaging of similar nearby pixel values may reduce the noise levels for the pixels. In some implementations, the operations of combining  920  the current image with recirculated image and applying  924  spatial noise reduction processing can be performed together as a single operation. 
     The technique  900  includes determining  930  a noise map for the noise reduced image (e.g., a noise reduced frame of video), where the noise map is determined based on estimates of noise levels for pixels in the current image (e.g., a current frame of video), a noise map for the recirculated image (e.g., a recirculate frame of video), and the set of mixing weights. For example estimates of noise level (e.g., standard deviations or variance) in the noise map may be determined  930  based on a sum of squares of the mixing weights that have been used to determine a respective image portion of the noise reduced image. For example, estimates of noise level in the noise map may be determined  930  using the Equation 2 or the Equation 4 above. 
     In some implementations, the noise map is stored at a resolution that is lower than a full resolution of the current image (e.g., a current frame of video) from the image sensor. For example, the noise map may store estimates of noise level for 2×2, 4×4, 8×8, 16×16, or 32×32 blocks of pixels. For example, the estimate of noise level for a block of pixels may be an average of estimates of the noise level for pixels within the block. In some implementations, the current image (e.g., a current frame of video) from the image sensor is stored in a raw Bayer mosaic format, and the noise map is stored as an array of noise level estimates respectively corresponding to two-by-two Bayer blocks of pixels in the current image from the image sensor. 
     Determining  930  the noise map may include adjusting the noise map based on a filter function used for spatial noise reduction processing. For example, noise map values may be adjusted based on a squares of coefficients of the filter function. 
     The technique  900  includes recirculating  940  the noise map with the noise reduced image (e.g., a noise reduced frame of video) to combine the noise reduced image with a next image of the sequence of images (e.g., a next frame of video) from the image sensor. In some implementations, recirculated frames are passed directly back unchanged to be combined  920  with the next image by a temporal noise reduction module. In some implementations, recirculating  940  a noise reduced image may include applying motion compensation to the noise reduced image to better align pixels of the recirculated image with corresponding pixels of the next image. For example, a motion compensation transformation (e.g., a local motion compensation transformation or a global motion compensation transformation) that is used to determine the recirculated image may also be used to update the noise map for the recirculated image. For example, the technique  1300  of  FIG.  13 A  may be implemented to recirculate the noise reduced image. 
     The technique  900  includes storing, displaying, or transmitting  950  an output image (e.g., and output frame of video) that is based on the noise reduced image (e.g., a noise reduced frame of video). For example, the output image may be transmitted  950  to an external device (e.g., a personal computing device) for display or storage. For example, the output image may be the same as the noise reduced image. For example, the output image may be a composite image determined by stitching an image based on the noise reduced image to one or more images from other image sensors with overlapping fields of view. For example, the output image may be compressed using an encoder (e.g., an MPEG encoder). For example, the output image may be transmitted  950  via the communications interface  618 . For example, the output image may be displayed in the user interface  620  or in the user interface  664 . For example, the output image may be stored in memory of the processing apparatus  612  or in memory of the processing apparatus  662 . 
     The technique  900  may be applied recursively to the sequence of images (e.g., a sequence of frames of video) from the image sensor. For example, a next set of mixing weights may be determined  910  based on the noise map for the noise reduced image based on the current image. The noise reduced image (e.g., a noise reduced frame of video) may then be combined  920  with the next image (e.g., a next frame of video) using the next set of mixing weights. 
     In some implementations (not shown) the technique  900  may be modified to reverse the order in which spatial noise reduction processing and temporal noise reduction processing are applied to the current image. These implementations include applying spatial noise reduction processing to the current frame before combining the current frame with the recirculated frame. In these implementations, the estimates of noise levels for pixels in the current frame include a noise map for the current frame that is generated based on initial estimates of noise levels for pixels in the current frame and a filter function used for spatial noise reduction processing. The noise map determined  930  for the noise reduced image may then be determined based on the noise map for the current image after spatial noise reduction processing, the noise map for the recirculated image, and the set of mix weights used for temporal noise reduction processing. 
       FIG.  10    is a flowchart of an example of a technique  1000  for determining mixing weights for temporal noise reduction. The technique  1000  includes determining  1010  a threshold based on a noise map value for an image portion (e.g., a pixel or bloc of pixels) of a recirculated image; determining  1012  a disparity between the image portion of the recirculated image and a corresponding image portion of a current image; comparing  1020  the disparity to the threshold; and, if (at  1023 ) the disparity is larger than the threshold, determining  1030  a mixing weight for the image portion to be zero, or, if (at  1023  &amp;  1027 ) the disparity is below the threshold and outside of a range near the threshold, determining  1040  the mixing weight based on the noise map, or, if (at  1023  &amp;  1027 ) the disparity is within the range near the threshold, determining  1050  an initial mixing weight based on the noise map, determining  1060  a scale factor based on the disparity, and determining  1070  the mixing weight based on the initial mixing weight and the scale factor. For example, the technique  1000  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1000  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1000  may be implemented by a personal computing device, such as the personal computing device  660 . 
     The technique  1000  includes determining  1010  a threshold for an image portion of the noise reduced image (e.g., a noise reduced frame) based on an estimate of noise level for the image portion of the noise reduced image from a noise map for the noise reduced image. For example, the threshold may be determined  1010  based on a sum of the estimate of noise level for the image portion of the noise reduced image and an estimate of noise level (e.g., a standard deviation or variance) for a corresponding image portion of the current image. For example, the threshold may be determined  1010  based on a maximum of the estimate of noise level for the image portion of the noise reduced image and an estimate of noise level (e.g., a standard deviation or variance) for a corresponding image portion of the current image. 
     The technique  1000  includes determining  1012  a disparity (e.g., a difference) between the image portion of the noise reduced image (e.g., a noise reduced frame of video) and a corresponding image portion of the current image (e.g., a current frame of video). For example, where the image portion is a pixel, the disparity may be determined  1012  as an absolute value of a difference between the value of the pixel in the recirculated image and the value of the corresponding pixel in the current image. For example, where the image portion is a block of pixels, the disparity may be determined  1012  as a maximum of the absolute values of differences between respective values of the pixels in the block of the recirculated image and the respective values of the corresponding pixels in the current image. For example, where the image portion is a block of pixels, the disparity may be determined  1012  as a sum of the absolute values of differences between respective values of the pixels in the block of the recirculated image and the respective values of the corresponding pixels in the current image. 
     The technique  1000  includes comparing  1020  the disparity to the threshold. If (at  1023 ) the disparity is greater than the threshold, then the mixing weight for the image portion of the recirculated image may be determined  1030  to be zero. For example, responsive to the disparity exceeding the threshold, a weight from the set of mixing weights corresponding to the image portion of the noise reduced image may be determined  1030  to be zero. Thus, in effect, this image portion of the recirculated image will be ignored or discarded when combining the recirculated image with the current image to obtain the noise reduced image. For example, the disparity exceeding a threshold based on the estimates of noise for the image portions may indicate that the scene has changed in a way that this image portion does not correspond to the same object in the two frames being combined. Combing the image portions when the viewed objects do not match can introduce errors and distortion. Selectively zeroing the mixing weight in this circumstance may improve image quality of the resulting noise reduced image. 
     If (at  1023  &amp;  1027 ) the disparity is less than the threshold and outside of a range near the threshold (e.g., within 5% or 10% of the threshold), then the mixing weight for the image portion of the recirculated image may be determined  1040  based on the noise map for recirculated image. For example, the mixing weight for the image portion may be determined  1040  based on a ratio of an estimate of noise level from the noise map for the image portion of the recirculated image to an estimate of noise level (e.g., from a noise map) for a corresponding image portion of the current image. For example, the mixing weight for the image portion may be determined  1040  as inversely proportional to an estimate of noise level from the noise map for the image portion of the recirculated image. 
     If (at  1023  &amp;  1027 ) the disparity is less than the threshold and within of a range near the threshold (e.g., within 5% or 10% of the threshold), then an initial weight may be determined  1050  based on the estimate of noise level (e.g., from the noise map) for the image portion of the recirculated image (e.g., a noise reduced frame of video) and an estimate of noise level for the corresponding image portion of the current image (e.g., a current frame of video). For example, the initial weight may be determined in the same manner as a weight is determined  1040  when the disparity is outside of the range. A scale factor is determined  1060  based on a difference between the disparity and the threshold. For example, one end of the range near the threshold may be the threshold, and the scale factor may be determined  1060  to vary linearly (e.g., taking values between zero and one) between endpoints of the range. The mixing weight for the image portion of the recirculated image may then be determined  1070  based on the initial weight and the scale factor. For example, responsive to the disparity being in a range near the threshold (e.g., within 5% or 10% of the threshold), a weight from the set of mixing weights corresponding to the image portion of the recirculated image (e.g., a noise reduced frame for video) may be determined  1070  to be the initial weight multiplied by a scale factor between zero and one. 
       FIG.  11    is a flowchart of an example of a technique  1100  for applying temporal noise reduction to high dynamic range images. The technique  1100  includes receiving  1110  images captured using different exposure times; applying  1112  motion compensation to better align corresponding pixels of the images; determining  1120  a high dynamic range image based on the images captured with different exposure times; determining  1130  a noise map for the high dynamic range image; applying  1140  temporal noise reduction processing to the high dynamic range image based on the noise map; and store, display, or transmit an output image that is based on the high dynamic range image. For example, the technique  1100  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1100  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1100  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  1100  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ) that is configured to perform image processing operations on the high dynamic range image. 
     The technique  1100  includes receiving  1110  a two or more images (e.g., frames of video) captured using different exposure times. For example, a first image and a second image may be received  1110  from an image sensor, where the first image is captured with a first exposure time and the second image is captured with a second exposure time that is less than the first exposure time. The image sensor may be part of an image capture apparatus (e.g., the image capture apparatus  110 , the image capture apparatus  300 , the image capture device  610 , or the image capture device  640 ). For example, the two or more images may be received  1110  from the image sensor via a bus (e.g., the bus  624  or image signal processor bus  590 ). In some implementations, the two or more images may be received  1110  via a communications link (e.g., the communications link  650 ). For example, the two or more images may be received  1110  via a wireless or wired communications interface (e.g., Wi-Fi, Bluetooth, USB, HDMI, Wireless USB, Near Field Communication (NFC), Ethernet, a radio frequency transceiver, and/or other interfaces). For example, the two or more images may be received  1110  via communications interface  666 . For example, the two or more images may be received  1110  via a front ISP (e.g., the front ISP  812 ) that performs some initial processing on the received images. For example, the two or more images may represent each pixel value in a defined format, such as in a RAW image signal format. For example, the two or more images be stored in a format using the Bayer color mosaic pattern. In some implementations, the two or more images may be frames of video. 
     The technique  1100  includes applying  1112  motion compensation to better align corresponding pixels of the images. For example, one of the two or more images may be used as a target image and motion compensation processing may be applied  1112  to the other images to better align pixels of the other images with corresponding pixels of the target image. In some implementations, one of the images captured with the longest exposure time may be used as the target image. In some implementations, one of the images captured with the shortest exposure time may be used as the target image. For example, the technique  1350  of  FIG.  13 B  may be implemented to apply  1112  motion compensation to one of the two or more images. For example, the techniques described in relation to  FIGS.  14 - 20    may be implemented to apply  1112  motion compensation to one of the two or more images. For example, the motion compensation module  730  of  FIG.  7    may be used to apply  1112  motion compensation to better align corresponding pixels of the images. For example, the motion compensation module  830  of  FIG.  8    may be used to apply  1112  motion compensation to better align corresponding pixels of the images. 
     The technique  1100  includes determining  1120  a high dynamic range image based on the two or more images captured with different exposure times. For example, image portions (e.g., pixels or blocks of pixels) of the high dynamic range image may be determined  1120  by selecting respective image portions from among the two or more images with the longest exposure time that do not exhibit pixel value saturation. For example, a high dynamic range image may be determined  1120  based on the first image (e.g., with a long exposure time) in a raw format and the second image (e.g., with a short exposure time) in a raw format, in which an image portion of the high dynamic range image is based on a corresponding image portion of the second image when a pixel of a corresponding image portion of the first image is saturated. For example, determining  1120  the high dynamic range image may include determining a blending ratio map that specifies how image portions of the two or more images will be combined to determine  1120  the high dynamic range image. In some implementations, a blending ratio map may be binary (e.g., specifying for an image portion that either the short exposure image portion will be used or a long exposure image portion will be used). In some implementations, spatial low-pass filtering may be applied to an initial blending ratio map to obtain a smoothed blending ration map. For example, the technique  1200  of  FIG.  12 A  may be implemented to determine  1120  the high dynamic range image. In some implementations, image portions from more than one of the two or more images are blended together to determine  1120  a respective image portion of the high dynamic range image when a pixel value of the image portion is in a range near a saturation level. For example, the technique  1250  of  FIG.  12 B  may be implemented to determine blending ratios for respective image portions of the high dynamic range image, and the blending ratios may be used to determine  1120  the high dynamic range image based on the two or more images captured with different exposure times. 
     The technique  1100  includes determining  1130  a noise map for the high dynamic range image. Because noise levels can vary with exposure time, the two or more images may be associated with different estimates of noise level for corresponding image portions (e.g., pixels or blocks of pixels). The noise map may include estimates of noise level for respective image portions of the high dynamic range image. The value of the noise map for a particular image portion may depend on which of the two or more images were selected as source for that image portion. For example, a noise map for the high dynamic range image may be determined  1130  based on noise level estimates for pixels of the first image (e.g., a long exposure image), noise level estimates for pixels of the second image (e.g., a short exposure image), and a blending ratio map that specifies how image components of the first image and the second image are combined to form the high dynamic range image. In some implementations, the blending ratio map may be binary, specifying whether an image portion from a long exposure image or an image portion from a short exposure image is used to determine  1120  the respective image portion of the high dynamic range image, and determining  1130  the noise map may include selecting amongst estimates of noise level for the long exposure image and the short exposure image for image portions based on the respective values of the blending ratio map. In some implementations, the blending ratio map may take on non-integer values, specifying blending image portions from a long exposure image and a short exposure image to determine  1120  a respective image component of the high dynamic range image, and determining  1130  the noise map may include calculating an estimate of noise level for an image portion based on the respective blending ratio and the corresponding estimates of noise level for the long exposure image and the short exposure image. For example, an estimate of noise level in the noise map for an image portion of the high dynamic range image may be determined as:
 
SHDR_ n =sqrt( b _ n {circumflex over ( )}2*SS_ n {circumflex over ( )}2+(1− b _ n ){circumflex over ( )}2*SL_ n {circumflex over ( )}2)  [Equation 5]
 
where SHDR_n is an estimate of noise level (e.g., a standard deviation) for an nth image portion of the high dynamic range image, b_n is a blending ratio for an nth image portion of the high dynamic range image, SS_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of a short exposure image, and SL_n is an estimate of noise level (e.g., a standard deviation) for the nth image portion of a long exposure image.
 
     The technique  1100  includes applying  1140  temporal noise reduction processing to the high dynamic range image based on the noise map. For example, the technique  900  of  FIG.  9    may be implemented to apply  1140  temporal noise reduction processing to the high dynamic range image using the noise map as an input noise map for the high dynamic range image. 
     The technique  1100  includes storing, displaying, or transmitting  1150  an output image (e.g., an output frame of video) that is based on the high dynamic range image (e.g., a high dynamic range frame of video). For example, the output image may be transmitted  1150  to an external device (e.g., a personal computing device) for display or storage. For example, the output image may be the same as the high dynamic range image. For example, the output image may be a composite image determined by stitching an image based on the high dynamic range image to one or more images from other image sensors with overlapping fields of view. For example, the output image may be compressed using an encoder (e.g., an MPEG encoder). For example, the output image may be transmitted  1150  via the communications interface  618 . For example, the output image may be displayed in the user interface  620  or in the user interface  664 . For example, the output image may be stored in memory of the processing apparatus  612  or in memory of the processing apparatus  662 . 
       FIG.  12 A  is a flowchart of an example of a technique  1200  for determining a high dynamic range image based on images captured with different exposure times. The technique  1200  includes determining  1210  initial blending ratios for respective image portions of the high dynamic range image to obtain an initial blending ratio map; applying  1220  a low-pass spatial filter to the initial blending ratio map to obtain a blending ratio map; and combining  1230  the first image and the second image using the blending ratio map to obtain the high dynamic range image. For example, the technique  1200  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1200  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1200  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  1200  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ) that is configured to perform image processing operations on the high dynamic range image. 
     The technique  1200  includes determining  1210  initial blending ratios for respective image portions of the high dynamic range image (e.g., a current image to be input to a temporal noise reduction module) to obtain an initial blending ratio map. In some implementations, the initial blending ratios are binary, specifying from which of two constituent images from the two or more images (e.g., a long exposure image and a short exposure image) the respective image portion (e.g., pixel or block of pixels) will be drawn. For example, blending ratios for image portions may be set to one (specifying that the short exposure image will be used for a respective image portion) if the corresponding image portion of a long exposure image has a saturated value and set to zero (specifying that the long exposure image will be used for a respective image portion) otherwise. In some implementations, the two or more images include N images with N&gt;2, where each of the N images is captured with a different exposure time. For example, the initial blending ratios may be dimension N vectors of binary variables having a single one (indicating one of the N images for the respective image portion) and the rest of the variables set to zero (indicating the other N−1 images are unused for the respective image portion). For an image portion, one of the N corresponding image portions with the longest exposure time that does not include a pixel with a saturated pixel value may be selected and have its respective element in the initial blending ratio vector set to one. Equivalently, the initial blending ratios may be dimension N−1 vectors of binary variables, and an Nth binary value for the blend of the Nth image (e.g., the image captured with the longest exposure time) may be implicitly specified as the compliment of the N−1 variables in the vector blending ratio. 
     In some implementations, the initial blending ratios may be allowed to take on values between zero and one and specify blending of two or more image portions from different images captured with different exposure times to obtain a respective image portion of the high dynamic range image. For example, the technique  1250  of  FIG.  12 B  may be implemented to determine  1210  an initial blending ratio for a respective image portion (e.g., a pixel or block of pixels) of the high dynamic range image. For example, where N images captured with N different exposure times are used to obtain the high dynamic range image, the initial blending ratios may be vectors of dimension N or, equivalently, the initial blending ratios may be vectors of dimension N−1 and an Nth value for the blend of the Nth image (e.g., the image captured with the longest exposure time) may be implicitly specified as the compliment of the N−1 variables in the vector blending ratio. 
     The technique  1200  includes applying  1220  a low-pass spatial filter to the initial blending ratio map to obtain a blending ratio map. For example, the low-pass spatial filter may be specified to calculate an average blending ratios over a block (e.g., a 9×9 block) from the initial blending ratio map. For example, the low-pass filter may be specified based on a radial basis function (e.g., a guassian function, an inverse quadratic function, or a polyharmonic spline function). In some implementations, an initial blending ratio map that includes blending ratios that are binary or vectors of binary variables may be mapped to integer values (e.g., zero or one) when the low-pass spatial filter is applied  1220  and the blending ratios of the obtained blending ration map may be allowed to take values between zero and one to specify blending of more than one image portion from different images captured with different exposure times to determine a respective image portion of the high dynamic range image. 
     The technique  1200  includes combining  1230  constituent images captured with different exposure times using the blending ratio map to obtain the high dynamic range image (e.g., a current image to be input to a temporal noise reduction module). For example, the technique  1200  may include combining  1230  the first image (e.g., a long exposure image) and the second image (e.g., a short exposure image) using the blending ratio map to obtain the high dynamic range image. For example, the first image and the second image may be combined  1230  using the blending ratio map according to:
 
HDR_ n=b _ n*S _ n +(1− b _ n )* L _ n   [Equation 6]
 
where HDR_n is an nth image portion of the high dynamic range image, b_n is a blending ratio for an nth image portion of a short exposure image captured with a short exposure time, S_n is the nth image portion of the short exposure image, and L_n is the nth image portion of a long exposure image captured with a long exposure time. For example, combining  1230  constituent images may include scaling (e.g., multiplying by a scale factor proportional to a respective exposure time for a constituent image) pixel values captured with different exposure times to occupy a common wider dynamic range for the high dynamic range image.
 
     In some implementations, an input noise map may be determined  1130  based on the blending ratio map. For example, the input noise map may be determined  1130  using the Equation 5 above. 
       FIG.  12 B  is a flowchart of an example of a technique  1250  for determining a blending ratio for an image portion of a high dynamic range image. The technique  1250  includes identifying  1260  a maximum pixel value for pixels in an image portion (e.g., a Bayer block of four pixels (one red, two green, one blue)) of a long exposure image, which was captured using a long exposure time; if (at  1263 ) the maximum pixel value is saturated, determining  1270  the blending ratio to be one (specifying that a corresponding image portion from a short exposure image will be used to determine the respective image component of the high dynamic range image); if (at  1263  &amp;  1267 ) the maximum pixel value is not saturated and is outside of a range near a saturation level, determining  1280  the blending ratio to be zero (specifying that a corresponding image portion from the long exposure image will be used to determine the respective image component of the high dynamic range image); and, if (at  1263  &amp;  1267 ) the maximum pixel value is not saturated and is within a range near the saturation level, determining  1290  a difference between the saturation level and the maximum pixel value and determining  1292  the blending ratio based on the difference. For example, the technique  1250  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1250  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1250  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  1250  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ) that is configured to perform image processing operations on the high dynamic range image. 
     The technique  1250  includes, responsive to a value of a pixel of an image component of the first image being in a range near a saturation level, determining  1292  a blending ratio based on the difference between the saturation level and the value of the pixel. For example, one end of the range near the saturation level may be the saturation level, and the blending ratio may be determined  1292  to vary (e.g., linearly) based on the difference (e.g., taking values between zero and one) between endpoints of the range. In some implementations, a corresponding image component of the high dynamic range image may be determined  1120  based on a weighted sum, using the blending ratio as a weight, of the image component of the first image and a corresponding image component of the second image. 
       FIG.  13 A  is a flowchart of an example of a technique  1300  for recirculating a noise map with a noise reduced image. The technique  1300  includes applying  1310  a motion compensation transformation to the recirculated image; and updating  1320  the noise map based on the motion compensation transformation. For example, the technique  1300  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1300  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1300  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  1300  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ). 
     The technique  1300  includes applying  1310  a motion compensation transformation to the recirculated image (e.g., a noise reduced frame). The motion compensation transformation may include a local motion transformation (e.g., as described in relation to the local motion compensation module  732 ) and/or a global motion transformation (e.g., as described in relation to the global motion compensation module  734 ). For example, the technique  1350  of  FIG.  13 B  may be implemented to select a motion compensation transformation that is applied  1310  to the recirculated image. In some implementations, the technique  1370  of  FIG.  13 C  may be implemented to obtain local motion information that may be used for applying  1310  the motion compensation transformation to the recirculated image. For example, the motion compensation module  730  of  FIG.  7    may be used to apply  1310  the motion compensation transformation to the recirculated image. For example, the motion compensation module  830  of  FIG.  8    may be used to apply  1310  the motion compensation transformation to the recirculated image. 
     The technique  1300  includes updating  1320  the noise map based on the motion compensation transformation. For example, the noise map for the recirculated image may be updated  1320  by applying the motion compensation transformation to the noise map. For example, estimates of noise level in the noise map corresponding to respective image portions (e.g., pixels or blocks of pixels) of the recirculated image (e.g., a noise reduced frame) may be translated within the noise map in the same way that the corresponding image portions are translated within the recirculated image when the motion compensation transformation is applied  1310 , such that a correspondence between estimates of noise level in the noise map and pixel values in the recirculated image (e.g., a noise reduced frame) may be preserved. 
       FIG.  13 B  is a flowchart of an example of a technique  1350  for applying motion compensation to a recirculated image. The technique  1350  includes applying  1352  a local motion compensation transformation to the reference image to obtain a first candidate image; applying  1354  a global motion compensation transformation to the reference image to obtain a second candidate frame; obtaining  1356  a first quality metric based on the first candidate image and the target image; obtaining  1358  a second quality metric based on the second candidate image and the target image; and based on the first quality metric and the second quality metric, selecting  1360  the motion compensation transformation from among the local motion compensation transformation and the global motion compensation transformation. For example, the technique  1350  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1350  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1350  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  1350  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ). 
     The technique  1350  includes applying  1352  a local motion compensation transformation to the reference image (e.g., a recirculated, noise reduced frame) to obtain a first candidate image (e.g., a first candidate frame). The local motion compensation transformation may be determined based on local motion information (e.g., a set of motion vectors) from a local motion estimation module (e.g., the local motion estimation unit  520 ). For example, the technique  1370  of  FIG.  13 C  may be implemented to obtain local motion information that may be used for applying  1352  the local motion compensation transformation to the reference image. For example, the local motion compensation transformation may be applied  1352  by the local motion compensation unit  522 . For example the local motion compensation transformation may be applied  1352  by the local motion compensation module  732 . 
     The technique  1350  includes applying  1354  a global motion compensation transformation to the reference image (e.g., a recirculated, noise reduced frame) to obtain a second candidate image (e.g., second candidate frame). For example, the global motion compensation transformation may be determined based on angular rate measurements (e.g., from a gyroscope) for an image capture apparatus used to capture a sequence of images being processed (e.g., a video). The angular rate measurements may be used to estimate a change in the orientation of the image capture apparatus between a time associated with the reference image and a time associated with a target image. For example, the global motion compensation transformation may affect a rotation of the scene that is determined based on angular rate measurements. For example, the global motion compensation transformation may be applied  1354  by the global motion compensation unit  524 . For example the global motion compensation transformation may be applied  1354  by the global motion compensation module  734 . 
     The technique  1350  includes obtaining  1356  a first quality metric based on the first candidate image (e.g., a first candidate frame of video) and the target image (e.g., next frame to be input to a temporal noise reduction unit). For example, the first quality metric may be a mean square pixel value of a difference between the target image and the first candidate image. For example, the first quality metric may be a perceptually weighted (e.g., using a pixel value mapping that models human perception) mean square error between target image and the first candidate image. In some implementations, the first quality metric may be based on a hit rate for image portions (e.g., pixels or blocks of pixels) of the first candidate image to be used by a temporal noise reduction module (e.g., the temporal noise reduction module  722 ). For example, the first metric may be based on a sum of mixing weights the image portions of the first candidate image that are to be used to combine the first candidate image with the target image (e.g., a current image being processed by a temporal noise reduction module). 
     The technique  1350  includes obtaining  1358  a second quality metric based on the second candidate image (e.g., a second candidate frame of video) and the target image (e.g., a next frame to be input to a temporal noise reduction unit). For example, the second quality metric may be a mean square pixel value of a difference between the target image and the second candidate image. For example, the second quality metric may be a perceptually weighted (e.g., using a pixel value mapping that models human perception) mean square error between target image and the second candidate image. In some implementations, the second quality metric may be based on a hit rate for image portions (e.g., pixels or blocks of pixels) of the second candidate image to be used by a temporal noise reduction module (e.g., the temporal noise reduction module  722 ). For example, the second metric may be based on a sum of mixing weights the image portions of the second candidate image that are to be used to combine the second candidate image with the target image (e.g., a current image being processed by a temporal noise reduction module). 
     The technique  1350  includes, based on the first quality metric and the second quality metric, selecting  1360  the motion compensation transformation from among the local motion compensation transformation and the global motion compensation transformation. The motion transformation corresponding to the best quality metric (e.g., the lowest mean square error metric or the highest sum of mixing weights) may be selected  1360 . For example, a noise map for the reference image may be updated  1320  based on the selected  1360  motion compensation transformation. 
     In some implementations (not shown in  FIG.  13 B ), a third quality metric is obtained based on the reference image (e.g., a recirculated, noise reduced frame) and the target image (e.g., a next frame to be input to a temporal noise reduction unit). This third quality metric may be associated with an identity transformation, i.e., passing reference image through unchanged. This modified technique may include; based on the first quality metric, the second quality metric, and the third quality metric; selecting the motion compensation transformation from among the local motion compensation transformation, the global motion compensation transformation, and an identity transformation. 
       FIG.  13 C  is a flowchart of an example of a technique  1370  for obtaining local motion information for a reference image and a target image. The technique  1370  includes selecting  1372  an image portion of a reference frame; obtaining  1374  a lowest resolution copy of the target image; identifying  1376  a search area in target image at the obtained resolution; searching  1378  for a match for the selected image portion within the identified search area; checking  1380  whether a highest resolution copy of the target image has been searched; if not, obtaining  1382  the next higher resolution copy of target image for performing a refined search by identifying  1376  a search in the higher resolution image near an area corresponding to a match found at the previous lower resolution and searching  1378  within the refined search area; and, if the highest resolution copy of the target image has been searched, then determining  1384  local motion information for the image portion of the reference image based a match found in the highest resolution copy of the target image. For example, the technique  1370  may be used to iteratively apply a multi-scale block matching approach to determine local motion information for a reference image (e.g., a recirculated image from a three-dimensional noise reduction module, a short exposure image, or a long exposure image and a target image (e.g., a current image to be input to a temporal noise reduction module, a long exposure image, or a short exposure image). 
     In some implementations, the technique  1370  may repeated for multiple image portions of the reference image to generate a set of motion vectors for the reference image that can be returned as local motion information. For example, the technique  1370  may be implemented by the system  600  of  FIG.  6 A  or the system  630  of  FIG.  6 B . For example, the technique  1370  may be implemented by an image capture device, such the image capture device  610  shown in  FIG.  6 A , or an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1    or the image capture apparatus  300  of  FIG.  3   . For example, the technique  1370  may be implemented by a personal computing device, such as the personal computing device  660 . For example, the technique  1370  may be implemented using a processing apparatus (e.g., the processing apparatus  612 ) that includes an image signal processor (e.g., the image signal processor  500 ). 
     The technique  1370  includes identifying  1376  a search area in target image at the obtained resolution. For example, the search area may be block within the target image at the obtained resolution that includes an image portion at a location corresponding to the selected  1372  image portion of the reference image and additional image portions in the vicinity of (e.g., within a radius from) the image portion at the corresponding location. For example, where the obtained image is at the lowest available resolution, the search area may include the entire target image at the lowest resolution. For example, where the obtained image is at higher resolution than a downscaled image previously searched  1378 , the search area may be identified  1376  based on the location of the matching portion found at the lower resolution. For example, the new search area may be identified  1376  to include image portions at a higher resolution within the matching portion found at the lower resolution. In some implementations, a next search area may be identified  1376  as extending slightly beyond (e.g., one image portion at the higher resolution beyond) the boundaries of the matching portion found at the lower resolution. In this manner a search area for a matching block at the full resolution may be iteratively narrowed as the resolution of the target image copies searched is increased. 
       FIG.  14    is a diagram of an example of a target image  1400  and an example of a corresponding one-half resolution downscaled image  1410 . The target image  1400  may be an input image, such as an input image received by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , from an image sensor, such as the image sensor  230  shown in  FIG.  2   , or from a front image signal processor, such as the front image signal processors  510  shown in  FIG.  5   . Receiving the input image may include reading the input image, or a portion thereof, from a memory, such as the electronic storage unit  224 . 
     The target image  1400  is represented as a 64×64 matrix of image portions (e.g., pixels or blocks of pixels). For simplicity and clarity, eight rows and eight columns of the 64×64 matrix representing the target image  1400  are shown in  FIG.  14   . The fourth column from the left and the fifth column from the left represent the fifty-six omitted rows and the fourth row from the top and the fifth row from the top represent the fifty-six omitted columns. Each location in the 64×64 matrix representing the target image  1400  corresponds with a respective spatial portion of the target image  1400 . For example, each location in the 64×64 matrix representing the target image  1400  may represent a pixel in the target image  1400 . 
     The one-half resolution downscaled image  1410  may be an input image, such as an input image received by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , from an image sensor, such as the image sensor  230  shown in  FIG.  2   , or from a front image signal processor, such as the front image signal processors  510  shown in  FIG.  5   . Receiving the input image may include reading the input image, or a portion thereof, from a memory, such as the electronic storage unit  224 . The one-half resolution downscaled image  1410  may be an image generated based on the target image  1400  by downscaling, or sub-sampling, the target image  1400 . For example, the one-half resolution downscaled image  1410  may be a ½×½ resolution frame corresponding to the target image. 
     In some implementations, the target image  1400  and the one-half resolution downscaled image  1410  may be may be obtained, such as generated, created, read, or otherwise accessed concurrently, or substantially concurrently. For example, a front image signal processor may concurrently, or substantially concurrently, output the target image  1400  and the one-half resolution downscaled image  1410 . 
     The one-half resolution downscaled image  1410  is represented as a 32×32 matrix of image portions (e.g., pixels or blocks of pixels). For simplicity and clarity, seven rows and seven columns of the 32×32 matrix representing the one-half resolution downscaled image  1410  are shown in  FIG.  14   . The fourth column from the left represents the twenty-five omitted rows and the fourth row from the top represents the twenty-five omitted columns. Each location in the 32×32 matrix representing the one-half resolution downscaled image  1410  corresponds with a respective spatial portion of the one-half resolution downscaled image  1410 . For example, each location in the 32×32 matrix representing the one-half resolution downscaled image  1410  may represent a pixel in the one-half resolution downscaled image  1410 . 
     The size of the 32×32 matrix representing the one-half resolution downscaled image  1410  is equivalent to size of the 64×64 matrix representing the target image  1400  to indicate that the field-of-view of the one-half resolution downscaled image  1410  is equivalent to the field-of-view of the 64×64 matrix representing the target image  1400 . The locations in the 32×32 matrix representing the one-half resolution downscaled image  1410  are larger than the locations in the 64×64 matrix representing the target image  1400  to indicate that a pixel from the one-half resolution downscaled image  1410  represents a larger, such as twice as large, spatial area than a pixel of the 64×64 matrix representing the target image  1400 . 
       FIG.  15    is a diagram of an example of a one-quarter resolution downscaled image  1500  and an example of a one-eighth resolution downscaled image  1510 . The one-quarter resolution downscaled image  1500  may be an input image, such as an input image received by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , from an image sensor, such as the image sensor  230  shown in  FIG.  2   , or from a front image signal processor, such as the front image signal processors  510  shown in  FIG.  5   . Receiving the input image may include reading the input image, or a portion thereof, from a memory, such as the electronic storage unit  224 . 
     The one-quarter resolution downscaled image  1500  may be an image generated based on a target image, such as the target image  1400  shown in  FIG.  14   , by downscaling, or sub-sampling, the target image. For example, the one-quarter resolution downscaled image  1500  may be a ¼×¼ resolution frame corresponding to the target image. 
     In some implementations, the target image and the one-quarter resolution downscaled image  1500  may be may be obtained, such as generated, created, read, or otherwise accessed concurrently, or substantially concurrently. For example, a front image signal processor may concurrently, or substantially concurrently, output the target image, a one-half resolution downscaled image, such as the one-half resolution downscaled image  1410  shown in  FIG.  14   , the one-quarter resolution downscaled image  1500 , or a combination thereof. 
     The one-quarter resolution downscaled image  1500  is represented as a 16×16 matrix of image portions (e.g., pixels or blocks of pixels). For simplicity and clarity, six rows and six columns of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  are shown in  FIG.  15   . The third column from the left and the fourth column from the left represent the ten omitted rows and the third row from the top and the fourth row from the top represent the ten omitted columns. Each location in the 16×16 matrix representing the one-quarter resolution downscaled image  1500  corresponds with a respective spatial portion of the one-quarter resolution downscaled image  1500 . For example, each location in the 16×16 matrix representing the one-quarter resolution downscaled image  1500  may represent a pixel in the one-quarter resolution downscaled image  1500 . 
     The size of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  is equivalent to the size of the 64×64 matrix representing the target image  1400  shown in  FIG.  14    and the size of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14    to indicate that the field-of-view of the one-quarter resolution downscaled image  1500  is equivalent to the field-of-view of the 64×64 matrix representing the target image  1400  shown in  FIG.  14    and the field-of-view of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   . The locations in the 16×16 matrix representing the one-quarter resolution downscaled image  1500  are larger than the locations in the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14    to indicate that a pixel from the one-quarter resolution downscaled image  1500  represents a larger, such as twice as large, spatial area than a pixel of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   . 
     The one-eighth resolution downscaled image  1510  may be an input image, such as an input image received by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , from an image sensor, such as the image sensor  230  shown in  FIG.  2   , or from a front image signal processor, such as the front image signal processors  510  shown in  FIG.  5   . Receiving the input image may include reading the input image, or a portion thereof, from a memory, such as the electronic storage unit  224 . 
     The one-eighth resolution downscaled image  1510  may be an image generated based on a target image, such as the target image  1400  shown in  FIG.  14   , by downscaling, or sub-sampling, the target image. For example, the one-eighth resolution downscaled image  1510  may be a ⅛×⅛ resolution frame corresponding to the target image. 
     In some implementations, the target image and the one-eighth resolution downscaled image  1510  may be may be obtained, such as generated, created, read, or otherwise accessed concurrently, or substantially concurrently. For example, a front image signal processor may concurrently, or substantially concurrently, output the target image, a one-half resolution downscaled image, such as the one-half resolution downscaled image  1410  shown in  FIG.  14   , the one-quarter resolution downscaled image  1500 , the one-eighth resolution downscaled image  1510 , or a combination thereof. 
     The one-eighth resolution downscaled image  1510  is represented as an 8×8 matrix of image portions (e.g., pixels or blocks of pixels). For simplicity and clarity, five rows and five columns of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  are shown in  FIG.  15   . The third column from the left represents the four omitted rows and the third row from the top represents the four omitted columns. Each location in the 8×8 matrix representing the one-eighth resolution downscaled image  1510  corresponds with a respective spatial portion of the one-eighth resolution downscaled image  1510 . For example, each location in the 8×8 matrix representing the one-eighth resolution downscaled image  1510  may represent a pixel in the one-eighth resolution downscaled image  1510 . 
     The size of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  is equivalent to the size of the 64×64 matrix representing the target image  1400  shown in  FIG.  14   , the size of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   , and the size of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  to indicate that the field-of-view of the one-eighth resolution downscaled image  1510  is equivalent to the field-of-view of the 64×64 matrix representing the target image  1400  shown in  FIG.  14   , the field-of-view of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   , and the field-of-view of the 16×16 matrix representing the one-quarter resolution downscaled image  1500 . The locations in the 8×8 matrix representing the one-eighth resolution downscaled image  1510  are larger than the locations in the 16×16 matrix representing the one-quarter resolution downscaled image  1500  to indicate that a pixel from the one-eighth resolution downscaled image  1510  represents a larger, such as twice as large, spatial area than a pixel of the 16×16 matrix representing the one-quarter resolution downscaled image  1500 . 
       FIG.  16    is a diagram of an example of a one-sixteenth resolution downscaled image  1600  and an example of a one-thirty-second resolution downscaled image  1610 . The one-sixteenth resolution downscaled image  1600  may be an input image, such as an input image received by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , from an image sensor, such as the image sensor  230  shown in  FIG.  2   , or from a front image signal processor, such as the front image signal processors  510  shown in  FIG.  5   . Receiving the input image may include reading the input image, or a portion thereof, from a memory, such as the electronic storage unit  224 . 
     The one-sixteenth resolution downscaled image  1600  may be an image generated based on a target image, such as the target image  1400  shown in  FIG.  14   , by downscaling, or sub-sampling, the target image. For example, the one-sixteenth resolution downscaled image  1600  may be a 1/16× 1/16 resolution frame corresponding to the target image. 
     In some implementations, the target image and the one-sixteenth resolution downscaled image  1600  may be may be obtained, such as generated, created, read, or otherwise accessed concurrently, or substantially concurrently. For example, a front image signal processor may concurrently, or substantially concurrently, output the target image, a one-half resolution downscaled image, such as the one-half resolution downscaled image  1410  shown in  FIG.  14   , a one-quarter resolution downscaled image, such as the one-quarter resolution downscaled image  1500  shown in  FIG.  15   , a one-eighth resolution downscaled image, such as the one-eighth resolution downscaled image  1510  shown in  FIG.  15   , the one-sixteenth resolution downscaled image  1600 , or a combination thereof. 
     The one-sixteenth resolution downscaled image  1600  is represented as a 4×4 matrix of image portions (e.g., pixels or blocks of pixels). Each location in the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600  corresponds with a respective spatial portion of the one-sixteenth resolution downscaled image  1600 . For example, each location in the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600  may represent a pixel in the one-sixteenth resolution downscaled image  1600 . 
     The size of the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600  is equivalent to the size of the 64×64 matrix representing the target image  1400  shown in  FIG.  14   , the size of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   , the size of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  shown in  FIG.  15   , and the size of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  shown in  FIG.  15    to indicate that the field-of-view of the one-sixteenth resolution downscaled image  1600  is equivalent to the field-of-view of the 64×64 matrix representing the target image  1400  shown in  FIG.  14   , the field-of-view of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   , the field-of-view of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  shown in  FIG.  15   , and the field-of-view of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  shown in  FIG.  15   . The locations in the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600  are larger than the locations in the 8×8 matrix representing the one-eighth resolution downscaled image  1510  shown in  FIG.  15    to indicate that a pixel from the one-sixteenth resolution downscaled image  1600  represents a larger, such as twice as large, spatial area than a pixel of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  shown in  FIG.  15   . 
     The one-thirty-second resolution downscaled image  1610  may be an input image, such as an input image received by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , from an image sensor, such as the image sensor  230  shown in  FIG.  2   , or from a front image signal processor, such as the front image signal processors  510  shown in  FIG.  5   . Receiving the input image may include reading the input image, or a portion thereof, from a memory, such as the electronic storage unit  224 . 
     The one-thirty-second resolution downscaled image  1610  may be an image generated based on a target image, such as the target image  1400  shown in  FIG.  14   , by downscaling, or sub-sampling, the target image. For example, the one-thirty-second resolution downscaled image  1610  may be a 1/32× 1/32 resolution frame corresponding to the target image. 
     In some implementations, the target image and the one-thirty-second resolution downscaled image  1610  may be may be obtained, such as generated, created, read, or otherwise accessed concurrently, or substantially concurrently. For example, a front image signal processor may concurrently, or substantially concurrently, output the target image, a one-half resolution downscaled image, such as the one-half resolution downscaled image  1410  shown in  FIG.  14   , a one-quarter resolution downscaled image, such as the one-quarter resolution downscaled image  1500  shown in  FIG.  15   , a one-eighth resolution downscaled image, such as the one-eighth resolution downscaled image  1510  shown in  FIG.  15   , the one-sixteenth resolution downscaled image  1600 , the one-thirty-second resolution downscaled image  1610 , or a combination thereof. 
     The one-thirty-second resolution downscaled image  1610  is represented as a 2×2 matrix of image portions (e.g., pixels or blocks of pixels). Each location in the 2×2 matrix representing the one-thirty-second resolution downscaled image  1610  corresponds with a respective spatial portion of the one-thirty-second resolution downscaled image  1610 . For example, each location in the 2×2 matrix representing the one-thirty-second resolution downscaled image  1610  may represent a pixel in the one-thirty-second resolution downscaled image  1610 . 
     The size of the 2×2 matrix representing the one-thirty-second resolution downscaled image  1610  is equivalent to the size of the 64×64 matrix representing the target image  1400  shown in  FIG.  14   , the size of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   , the size of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  shown in  FIG.  15   , the size of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  shown in  FIG.  15   , and the size of the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600  to indicate that the field-of-view of the one-thirty-second resolution downscaled image  1610  is equivalent to the field-of-view of the 64×64 matrix representing the target image  1400  shown in  FIG.  14   , the field-of-view of the 32×32 matrix representing the one-half resolution downscaled image  1410  shown in  FIG.  14   , the field-of-view of the 16×16 matrix representing the one-quarter resolution downscaled image  1500  shown in  FIG.  15   , the field-of-view of the 8×8 matrix representing the one-eighth resolution downscaled image  1510  shown in  FIG.  15   , and the field-of-view of the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600 . The locations in the 2×2 matrix representing the one-thirty-second resolution downscaled image  1610  are larger than the locations in the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600  to indicate that a pixel from the one-thirty-second resolution downscaled image  1610  represents a larger, such as twice as large, spatial area than a pixel of the 4×4 matrix representing the one-sixteenth resolution downscaled image  1600 . 
       FIG.  17    is a flowchart of an example of a technique  1700  for motion compensation. In some implementations, the technique  1700  may be implemented in an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , or the image capture apparatus  300  shown in  FIG.  3   . For example, aspects of motion compensation included in the technique  1700  may be implemented by one or more of a local motion estimation unit, such as the local motion estimation unit  520  shown in  FIG.  5   , a local motion compensation unit, such as the local motion compensation unit  522  shown in  FIG.  5   , a global motion compensation unit, such as the global motion compensation unit  524  shown in  FIG.  5   , a temporal noise reduction unit, such as the temporal noise reduction unit  542  of the image signal processor  500  shown in  FIG.  5   , a high dynamic range unit, such as the high dynamic range unit  530  of the image signal processor  500  shown in  FIG.  5   , or a combination thereof. 
     The technique  1700  may include obtaining a target image at  1710 , obtaining a reference image at  1720 , obtaining motion compensation information at  1730 , obtaining a processed image at  1740 , outputting the processed image at  1750 , or a combination thereof. In some implementations, one or more aspects of motion compensation described in relation to the technique  1700  may be omitted or combined, and one or more other aspects, not shown in  FIG.  17   , may be included. 
     A target image may be obtained at  1710 . Obtaining the target image at  1710  may include receiving, such as by an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , which may be included in an image capture apparatus, one or more input image signals, such as the input image signal  430  shown in  FIG.  4   , from one or more image sensors, such as the image sensor  230  shown in  FIG.  2    or the image sensors  340 ,  342  shown in  FIG.  3   , or from one or more front image signal processors, such as the front image signal processors  510  shown in  FIG.  5   , and identifying one or more input images, or frames, from the input image signals, which may include buffering the input images or frames. In some implementations, the input images or frames may be associated with respective temporal information indicating a respective temporal location, such as a time stamp, a date stamp, sequence information, or a combination thereof. For example, the input images or frames may be included in a stream, sequence, or series of input images or frames, such as a video, and each input image or frame may be associated with respective temporal information. In some implementations, such as implementations including high dynamic range processing, the target image may be a long exposure frame. In some implementations, such as implementations including high dynamic range processing, the target image may be a short exposure frame. 
     The target image may have a full size or resolution, which may be the resolution of the image as captured. For example, the target image, or frame, may be a 3840×2160 image, which may include 3840 columns (W=3840), or pixels per row, and 2160 rows (H=2160). In some implementations, obtaining the target image at  1710  may include obtaining one or more downscaled target images corresponding to the target image. For example, an image signal processor, such as the image signal processor  410  shown in  FIG.  4    or the image signal processor  500  shown in  FIG.  5   , may obtain the target image from a front image signal processor, such as the front image signal processor  510  shown in  FIG.  5   , may include obtaining the target image at a full resolution as captured, one or more downscaled, or reduced, resolution frames, such as a one-half resolution frame, a one-quarter resolution frame, a one-eighth resolution frame, a one-sixteenth resolution frame, a one-thirty-second resolution frame, or any combination thereof. Other resolutions may be used. 
     A reference image may be obtained at  1720 . In some implementations, such as implementations including temporal noise reduction, the reference frame may be a previously processed frame, such as reconstructed or recirculated frame, which may be a frame temporally preceding the target image obtained at  1710 . In some implementations, such as implementations including high dynamic range processing, the reference image may be a short exposure frame corresponding to the target image (e.g., a long exposure frame) obtained at  1710 . In some implementations, such as implementations including high dynamic range processing, the reference image may be a long exposure frame corresponding to the target image (e.g., a short exposure frame) obtained at  1710 . 
     Motion compensation information may be obtained at  1730 . Obtaining the motion compensation information may include obtaining global motion compensation information at  1732 , obtaining local motion compensation information at  1734 , or a combination thereof. 
     Global motion compensation information may be obtained at  1732 . For example, a global motion compensation unit, such as the global motion compensation unit  524  shown in  FIG.  5   , may obtain or generate the global motion compensation information. 
     Obtaining the global motion compensation information at  1732  may include receiving, or otherwise accessing, the reference image, or one or more portions thereof. In some implementations, such as implementations implementing high dynamic range image processing, the reference image may be the short exposure input frame. 
     Obtaining the global motion compensation information at  1732  may include receiving, or otherwise accessing, global motion information, such as global motion information from a gyroscopic unit of the image capture apparatus, such as a gyroscopic sensor included in the metadata unit  232  shown in  FIG.  2   , corresponding to the target image. For example, the global motion information may indicate global motion detected or determined between capturing the reference image and capturing the target image. 
     Obtaining the global motion compensation information at  1732  may include generating or obtaining a global motion prediction frame or image, or a portion thereof, such as a prediction block, which may be a prediction of the target image, or a portion thereof, such as a target block of the target image, based on the reference image, or a portion thereof, and the global motion information. Obtaining the global motion compensation information at  1732  may include outputting, or otherwise producing, global motion compensation information, such as a global motion compensated prediction image, or one or more portions thereof, which may be referred to herein as a global motion compensated frame or image. 
     Local motion compensation information may be obtained at  1734 . For example, a local motion compensation unit, such as the local motion compensation unit  522  shown in  FIG.  5   , may obtain or generate the local motion compensation information. 
     Obtaining the local motion compensation information at  1734  may include receiving, or otherwise accessing, the reference image, or one or more portions thereof. In some implementations, such as implementations implementing high dynamic range image processing, the reference image may be the short exposure input frame. 
     Obtaining the local motion compensation information at  1734  may include receiving, or otherwise accessing, local motion information, such as local motion information from a local motion estimation unit, such as the local motion estimation unit  520  shown in  FIG.  5   , corresponding to the target image. For example, the local motion information may indicate local motion identified between the reference image and the target image. 
     Obtaining the local motion compensation information at  1734  may include generating or obtaining a local motion prediction frame or image, or a portion thereof, such as a prediction block, which may be a prediction of the target image, or a portion thereof, such as a target block of the target image, based on the reference image, or a portion thereof, and the local motion information. Obtaining the local motion compensation information at  1734  may include outputting, or otherwise producing, local motion compensation information, such as a local motion compensated prediction image, or one or more portions thereof, which may be referred to herein as a local motion compensated frame or image. Examples of generating local motion compensation information are shown in  FIGS.  19 - 20   . 
     A processed image may be obtained or generated at  1740 . Obtaining the processed image at  1740  may include obtaining, such as by receiving, the target image, obtaining, such as by receiving, the local motion compensation information, obtain, such as by receiving, the global motion compensation information, or a combination thereof. For example, a temporal noise reduction unit, such as the temporal noise reduction unit  542  shown in  FIG.  5   , may obtain the target image, the location motion compensation information, and the global motion compensation information, and may determine whether to use the local motion compensation information, the global motion compensation information, or both, to generate the processed image (e.g., a temporal noise reduced image) at  1740 . In another example, a high dynamic range unit, such as the high dynamic range unit  530  shown in  FIG.  5   , may obtain the target image, the location motion compensation information, and the global motion compensation information, and may determine whether to use the local motion compensation information, the global motion compensation information, or both, to generate the processed image (e.g., a high dynamic range image) at  1740 . An example of determining whether to use the local motion compensation information, the global motion compensation information, or both, to generate the processed image is shown in  FIG.  18   . 
     The processed image, or a portion thereof, such as a block of the processed image, may be output at  1750 . For example, outputting the processed image at  1750  may include storing the processed image in a memory (e.g., the electronic storage unit  224 ), or outputting the processed image directly to another image signal processing unit, such as the temporal noise reduction unit  542  shown in  FIG.  5   . In some implementations, such as implementations including high dynamic range processing, outputting the processed image at  1750  may include outputting a high dynamic range image. In some implementations, outputting the processed image at  1750  may include outputting a noise reduced image. 
       FIG.  18    is a flowchart of an example of a technique for  1800  determining whether to use local motion compensation information or global motion compensation information. In some implementations, determining whether to use local motion compensation information or global motion compensation information  1800  may be implemented in an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , or the image capture apparatus  300  shown in  FIG.  3   . For example, a temporal noise reduction unit, such as the temporal noise reduction unit  542  of the image signal processor  500  shown in  FIG.  5   , may implement determining whether to use local motion compensation information or global motion compensation information  1800 , or a high dynamic range unit, such as the high dynamic range unit  530  of the image signal processor  500  shown in  FIG.  5   , may implement determining whether to use local motion compensation information or global motion compensation information  1800 . 
     Image signal processing may include obtaining a processed image (e.g., a noise reduced image or a high dynamic range image) at  1810 , which may be similar to obtaining a processed image as show at  1740  in  FIG.  17   , and which may implement determining whether to use local motion compensation information or global motion compensation information  1800 . Obtaining the processed image at  1810  may include obtaining a first, or local prediction, image quality metric at  1820 , obtaining a second, or global prediction, image quality metric at  1830 , identifying a best, or optimal, image quality metric at  1840 , obtaining local motion compensation information at  1850 , obtaining global motion compensation information at  1860 , or a combination thereof. Although not shown separately in  FIG.  18   , obtaining a processed image at  1810  may include obtaining the target image, obtaining the local motion compensated image, and obtaining the global motion compensated image. 
     A first image quality metric, such as a local prediction image quality metric, may be obtained at  1820 . Obtaining the local prediction image quality metric at  1820  may include determining a difference, such as a sum of absolute differences, between the target image, or a portion thereof, such as a target block of the target image, and a corresponding local motion compensated image, or a portion thereof, such as a corresponding local motion compensated prediction block. 
     A second image quality metric, such as a global prediction image quality metric, may be obtained at  1830 . Obtaining the global prediction image quality metric at  1830  may include determining a difference, such as a sum of absolute differences, between the target image, or a portion thereof, such as a target block of the target image, and a corresponding global motion compensated image, or a portion thereof, such as a corresponding global motion compensated prediction block. 
     A best, minimal, or optimal, image quality metric may be obtained, or identified, at  1840 . Obtaining the best image quality metric at  1840  may include determining whether the local prediction image quality metric obtained at  1820  is within the global prediction image quality metric obtained at  1830 . For example, obtaining the best image quality metric at  1840  may include determining whether the local prediction image quality metric obtained at  1820  is less than or equal to the global prediction image quality metric obtained at  1830 . 
     Obtaining the best image quality metric at  1840  may include determining that the local prediction image quality metric obtained at  1820  is within, such as is less than or equal to, the global prediction image quality metric obtained at  1830 , which may indicate that the local motion compensation information more accurately, or more efficiently, predicts the target image, or the target portion of the target image, than the global motion compensation information, and the local motion compensation information may be used for generating or obtaining the processed image at  1850 . 
     In another example, the local prediction image quality metric obtained at  1820  may exceed the global prediction image quality metric obtained at  1830 , which may indicate that the global motion compensation information more accurately, or more efficiently, predicts the target image, or the target portion of the target image, than the local motion compensation information, and the global motion compensation information may be used for generating or obtaining the processed image at  1860 . 
       FIG.  19    is a flowchart of an example of a technique  1900  for obtaining local motion information. In some implementations, the technique  1900  may be implemented in an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , or the image capture apparatus  300  shown in  FIG.  3   . For example, a local motion estimation unit, such as the local motion estimation unit  520  of the image signal processor  500  shown in  FIG.  5   , may implement the technique  1900 . 
     Image signal processing may include obtaining location motion compensation information at  1910 , which may be similar to obtaining location motion compensation information as shown at  1734  in  FIG.  17   , and which may implement the technique  1900 . The technique  1900  may include obtaining a downscaled image at  1920 , obtaining motion information for the downscaled image at  1930 , determining whether the downscaled image is the highest resolution downscaled image available at  1940 , obtaining motion information for the target image at  1950 , or a combination thereof. 
     A downscaled image may be obtained at  1920 . For example, the technique  1900  may be implemented in an image signal processor, such as the image signal processor  500  shown in  FIG.  5   , of an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , or the image capture apparatus  300  shown in  FIG.  3   , which may include one or more front image signal processors, such as the front image signal processor  510  shown in  FIG.  5   , which may output the target image and one or more downscaled, or reduced, resolution images based on the target image, such as a ½×½ resolution frame, a ¼×¼ resolution frame, a ⅛×⅛ resolution frame, a 1/16× 1/16 resolution frame, a 1/32× 1/32 resolution frame, or any combination thereof, and obtaining the downscaled image at  1920  may include obtaining the downscaled image from the downscaled images generated by the front image signal processor in order of increasing resolution. 
     For example, obtaining the downscaled images at  1920  may include obtaining a lowest resolution downscaled image, such as by reading the lowest resolution downscaled image from a memory. For example, the lowest resolution downscaled image may be a one-thirty-second resolution downscaled image, such as the one-thirty-second resolution downscaled image  1610  shown in  FIG.  16   . 
     Motion information for the downscaled image may be obtained at  1930 . An example of obtaining motion information for a target image, such as the downscaled image is shown in  FIG.  20   . 
     Whether the downscaled image obtained at  1920  is the highest resolution downscaled image for the target image may be determined at  1940 . For example, one downscaled image may be available for local motion compensation, and determining whether the downscaled image is the highest resolution downscaled image for the target image at  1940  may include determining that the downscaled image is the highest resolution downscaled image for the target image. In another example, a downscaled image having a higher resolution than the downscaled image identified at  1920  may be available for local motion compensation, and determining whether the downscaled image is the highest resolution downscaled image for the target image may at  1940  may include determining that the downscaled image is not the highest resolution downscaled image for the target image. 
     The downscaled image may not be the highest resolution downscaled image for the target image, a downscaled image having a higher resolution than the downscaled image identified at  1920  may be available for local motion compensation, and obtaining the downscaled image at  1920 , obtaining motion information for the downscaled image at  1930 , and determining whether a higher resolution downscaled image is available at  1940  may be performed using the higher resolution downscaled image as the downscaled image as indicated by the broken line at  1945 . 
     For example, the downscaled images may include a one-half resolution image, a one-quarter resolution image, a one-eighth resolution image, a one-sixteenth resolution image, and a one-thirty-second resolution image, and the technique  1900  may include obtaining the one-thirty-second resolution downscaled image at  1920 , obtaining motion information for the one-thirty-second resolution downscaled image at  1930 , and determining that a higher resolution downscaled image is available at  1940 , obtaining the one-sixteenth resolution downscaled image at  1920 , obtaining motion information for the one-sixteenth resolution downscaled image at  1930 , and determining that a higher resolution downscaled image is available at  1940 , obtaining the one-eighth resolution downscaled image at  1920 , obtaining motion information for the one-eighth resolution downscaled image at  1930 , and determining that a higher resolution downscaled image is available at  1940 , obtaining the one-quarter resolution downscaled image at  1920 , obtaining motion information for the one-quarter resolution downscaled image at  1930 , and determining that a higher resolution downscaled image is available at  1940 , and obtaining the one-half resolution downscaled image at  1920 , obtaining motion information for the one-half resolution downscaled image at  1930 , and determining that a higher resolution downscaled image is unavailable at  1940 . 
     Motion information for the target image may be obtained at  1950 . An example of obtaining motion information for a target image is shown in  FIG.  20   . 
       FIG.  20    is a flowchart of an example of a technique  2000  for obtaining local motion information for a target image. In some implementations, the technique  2000  may be implemented in an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , or the image capture apparatus  300  shown in  FIG.  3   . For example, a local motion estimation unit, such as the local motion estimation unit  520  of the image signal processor  500  shown in  FIG.  5   , may implement the technique  2000 . 
     Image signal processing may include obtaining location motion compensation information at  2010 , which may be similar to obtaining location motion compensation information as shown at  1734  in  FIG.  17   , and which may implement the technique  2000 . The technique  2000  may include obtaining a target image at  2020 , obtaining an image portion at  2030 , obtaining a search area in a reference image at  2040 , obtaining a matching portion at  2050 , obtaining the motion information at  2060 , or a combination thereof. 
     A target image may be obtained at  2020 . For example, the technique  2000  may be implemented in an image signal processor, such as the image signal processor  500  shown in  FIG.  5   , of an image capture apparatus, such as the image capture apparatus  110  shown in  FIG.  1   , or the image capture apparatus  300  shown in  FIG.  3   , which may include one or more front image signal processors, such as the front image signal processor  510  shown in  FIG.  5   , which may output the target image, which may be a current input frame and/or downscaled, or reduced, resolution frames, such as a ½×½ resolution frame, a ¼×¼ resolution frame, a ⅛×⅛ resolution frame, a 1/16× 1/16 resolution frame, a 1/32× 1/32 resolution frame, and obtaining the target image at  2020  may include obtaining the target image from the images generated by the front image signal processor. 
     The target image obtained at  2020  may be a lowest resolution downscaled image for a target image, such as a one-thirty-second resolution downscaled image, such as the one-thirty-second resolution downscaled image  1610  shown in  FIG.  16   , and obtaining the target image may omit obtaining motion information corresponding to a lower resolution downscaled image. 
     The target image obtained at  2020  may be a higher resolution downscaled image, having a higher resolution than the lowest resolution downscaled image, or may be the target image, and obtaining the target image at  2020  may include obtaining motion information, such as motion vectors, previously generated or identified based on a lower resolution downscaled image for the target image. For example, the target image obtained at  2020  may be a one-sixteenth resolution downscaled image and obtaining the target image at  2020  may include obtaining previously generated motion information for a lower resolution downscaled image, such as the one-thirty-second resolution downscaled image. 
     The technique  2000  may include obtaining  2030  an input image portion. For example, a first input portion from a downscaled target image may be obtained  2030 , wherein the first input portion has a first input portion location in the downscaled target image and a first input portion size. For example, a second input portion may be obtained  2030  from the target image, wherein the second input portion has the first input portion location in the target image and a second input portion size. 
     The technique  2000  may include obtaining  2040  a search area in a reference image. For example, a first search area portion may be obtained  2040  from the reference image, wherein the first search area portion is centered on a first location in the reference image that corresponds with the first input portion location, and wherein the first search area portion has a first search area portion size that exceeds the first input portion size by a first defined search area amount associated with the resolution of the downscaled target image. For example, a second search area portion may be obtained  2040  from the reference image, wherein the second search area portion is centered on a second location in the reference image that is indicated by the first input portion location and the first local motion vector, and wherein the first search area portion has a first search area portion size that exceeds the first input portion size by a first defined search area amount associated with the resolution of the downscaled target image. For example, a search area portion may be obtained  2040  from the reference image, wherein the search area portion is centered on a location in the reference image indicated by the input portion location and a candidate local motion information corresponding to a next lower resolution downscaled target image, and wherein the search area portion has a search area portion size that exceeds the input portion size by a defined search area amount associated with the resolution of the downscaled target image. 
     The technique  2000  may include obtaining  2050  a matching portion. For example, a first matching portion may be obtained  2050  from the first search area portion by searching the first search area portion based on the first input portion, wherein the first matching portion has the first input portion size. For example, a second matching portion may be obtained  2050  from the second search area portion by searching the second search area portion based on the second input portion, wherein the second matching portion has the second input portion size. 
     The technique  2000  may include obtaining  2060  the motion information. For example, a first local motion vector may be obtained  2060  that indicates a spatial difference between the first input portion location and a location of the first matching portion in the reference image as the first local motion estimation information. For example, a second local motion vector may be obtained  2060  that indicates a spatial difference between the second input portion location and a location of the second matching portion in the reference image as the second local motion estimation information. For example, a local motion vector may be obtained  2060  that indicates a spatial difference between the input portion location and a location of the matching portion in reference image as a candidate local motion information for the downscaled target image. 
     For example, a first implementation may include a non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: obtaining, by an image signal processor, a target image; obtaining, by the image signal processor, a reference image; obtaining motion compensation information indicating motion identified between the target image and the reference image, wherein obtaining the motion compensation information includes obtaining local motion compensation information and obtaining global motion compensation information; obtaining a processed image by updating the target image based on the motion compensation information; and outputting the processed image. 
     For example, in the first implementation, obtaining the motion compensation information may include determining whether to use the local motion compensation information or the global motion compensation information. 
     For example, in the first implementation, determining whether to use the local motion compensation information or the global motion compensation information may include: obtaining a first image quality metric based on the local motion compensation information; obtaining a second image quality metric based on the global motion compensation information; identifying the local motion compensation information as the motion compensation information on a condition that the second image quality metric exceeds the first image quality metric; and identifying the global motion compensation information as the motion compensation information on a condition that the first image quality metric exceeds the second image quality metric. 
     For example, in the first implementation, obtaining the local motion compensation information may include: obtaining a first local motion estimation information based on the target image and the reference image; and obtaining a second local motion estimation information based on the target image, the reference image, and the first local motion estimation information. For example, obtaining the first local motion estimation information may include: obtaining a downscaled target image corresponding to the target image, wherein a resolution of the target image exceeds a resolution of the downscaled target image; obtaining a first input portion from the downscaled target image, wherein the first input portion has a first input portion location in the downscaled target image and a first input portion size; obtaining a first search area portion from the reference image, wherein the first search area portion is centered on a first location in the reference image that corresponds with the first input portion location, and wherein the first search area portion has a first search area portion size that exceeds the first input portion size by a first defined search area amount associated with the resolution of the downscaled target image; obtaining a first matching portion from the first search area portion by searching the first search area portion based on the first input portion, wherein the first matching portion has the first input portion size; and obtaining a first local motion vector indicating a spatial difference between the first input portion location and a location of the first matching portion in the reference image as the first local motion estimation information. For example, obtaining the second local motion estimation information may include: obtaining a second input portion from the target image, wherein the second input portion has the first input portion location in the target image and a second input portion size; obtaining a second search area portion from the reference image, wherein the second search area portion is centered on a second location in the reference image that is indicated by the first input portion location and the first local motion vector, and wherein the first search area portion has a first search area portion size that exceeds the first input portion size by a first defined search area amount associated with the resolution of the downscaled target image; obtaining a first matching portion from the first search area portion by searching the first search area portion based on the first input portion, wherein the first matching portion has the first input portion size; and obtaining a first local motion vector indicating a spatial difference between the first input portion location and a location of the first matching portion in the reference image as the first local motion estimation information. 
     For example, in the first implementation, the target frame may be a long exposure frame, and the reference frame is a short exposure frame corresponding to the long exposure frame for high dynamic range processing. 
     For example, in the first implementation, the target frame may be a first frame from a sequence of frames; the target frame may have a first location in the sequence of frames; the reference frame may be a second frame from the sequence of frames; and the reference frame may have a second location in the sequence of frames preceding the first location in the sequence of frames. 
     For example, a second implementation may include a non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: obtaining, by an image signal processor, a target image; obtaining, by the image signal processor, a reference image; obtaining motion compensation information indicating motion identified between the target image and the reference image, wherein obtaining the motion compensation information includes obtaining local motion compensation information and obtaining global motion compensation information; obtaining a processed image by updating the target image based on the motion compensation information; outputting the processed image; and wherein obtaining the local motion compensation information may include obtaining the local motion compensation information using multiscale local motion estimation, wherein multiscale local motion estimation includes: obtaining downscaled target images corresponding to the target image, wherein a resolution of the target image exceeds a respective resolution of each downscaled target image from the downscaled target images, and wherein the respective resolution of each downscaled target image from the downscaled target images differs from the respective resolution of each other downscaled target image from the downscaled target images; for each downscaled target image from the downscaled target images in order of increasing resolution: obtaining candidate local motion information for the downscaled target image, wherein obtaining the candidate local motion information for the downscaled target image includes: on a condition that a resolution of the downscaled target image is a lowest resolution among the downscaled target images, obtaining the candidate local motion information for the downscaled target image based on the downscaled target image and the reference image; and on a condition that a resolution of the downscaled target image is greater than the lowest resolution among the downscaled target images, obtaining the candidate local motion information for the downscaled target image based on the downscaled target image, the reference image, and candidate local motion information corresponding to a next lower resolution downscaled target image from the downscaled target images; and obtaining local motion estimation information based on the target image, the reference image, and candidate local motion estimation information corresponding to a downscaled target image from the downscaled target images that has a highest resolution among the downscaled target images. 
     For example, in the second implementation, on the condition that the resolution of the downscaled target image is the lowest resolution among the downscaled target images, obtaining the candidate local motion information for the downscaled target image based on the downscaled target image and the reference image includes: obtaining an input portion from the downscaled target image, wherein the input portion has an input portion location in the downscaled target image and an input portion size; obtaining a search area portion from the reference image, wherein the search area portion is centered on a location in the reference image that corresponds with the input portion location, and wherein the search area portion has a search area portion size that exceeds the input portion size by a defined search area amount associated with the resolution of the downscaled target image; obtaining a matching portion from the search area portion by searching the search area portion based on the input portion, wherein the matching portion has the input portion size; and obtaining a local motion vector indicating a spatial difference between the input portion location and a location of the matching portion in reference image as the candidate local motion information for the downscaled target image. 
     For example, in the second implementation, on the condition that the resolution of the downscaled target image is greater than the lowest resolution among the downscaled target images, obtaining the candidate local motion information for the downscaled target image based on the downscaled target image, the reference image, and the candidate local motion information corresponding to the next lower resolution downscaled target image may include: obtaining an input portion from the downscaled target image, wherein the input portion has an input portion location in the downscaled target image and an input portion size; obtaining a search area portion from the reference image, wherein the search area portion is centered on a location in the reference image indicated by the input portion location and the candidate local motion information corresponding to the next lower resolution downscaled target image, and wherein the search area portion has a search area portion size that exceeds the input portion size by a defined search area amount associated with the resolution of the downscaled target image; obtaining a matching portion from the search area portion by searching the search area portion based on the input portion, wherein the matching portion has the input portion size; and obtaining a local motion vector indicating a spatial difference between the input portion location and a location of the matching portion in reference image as the candidate local motion information for the downscaled target image. 
     For example, in the second implementation, obtaining local motion estimation information based on the target image, the reference image, and the candidate local motion estimation information corresponding to the downscaled target image from the downscaled target images that has the highest resolution among the downscaled target images may include: obtaining an input portion from the target image, wherein the input portion has an input portion location in the target image and an input portion size; obtaining a search area portion from the reference image, wherein the search area portion is centered on a location in the reference image indicated by the input portion location and the candidate local motion information corresponding to the downscaled target image from the downscaled target images that has the highest resolution among the downscaled target images, and wherein the search area portion has a search area portion size that exceeds the input portion size by a defined search area amount associated with the resolution of the target image; obtaining a matching portion from the search area portion by searching the search area portion based on the input portion, wherein the matching portion has the input portion size; and obtaining a local motion vector indicating a spatial difference between the input portion location and a location of the matching portion in reference image as the local motion information for the target image. 
     For example, in the second implementation, multiscale local motion estimation may include: obtaining the downscaled target images by generating the downscaled target images by a front image signal processor concurrent with generating the target image by the front image signal processor. 
       FIG.  21    illustrates an example of an architecture  2100  for processing and stitching images captured with multiple image sensors. The architecture  2100  includes two image sensors ( 2110 A and  2110 B) configured to capture two images (image A and image B, respectively) with at least partially overlapping fields of view. The architecture  2100  further includes a memory  2116 , configured to store the raw (or unprocessed) images (e.g., the image A and the image B). In addition, the architecture  2100  includes an image signal processor (“ISP”)/stitch engine  2122 , configured to request (via read/request signal) access to the raw images (e.g., the image A and the image B) from the memory  2116 , to process and stitch the images (e.g., the image A and the image B), and to store the processed and stitched images (via write signal) in a memory  2126  of the architecture  2100 . For example, the architecture  2100  may be implemented by an image capture apparatus (e.g., the image capture apparatus  110  of  FIG.  1    or the image capture apparatus  300  of  FIG.  3   ). 
     For example, the architecture  2100  may be implemented within one camera system, such as, a singular device having two capture mechanisms (e.g., two lenses, two image sensors, two capture controllers, etc.) but having one ISP/stitching engine  2122 . In some implementations, the architecture  2100  includes two separate camera systems. For example, either an ISP of one of the cameras or an external ISP or processing system can perform the functions of the ISP/stitch engine  2122 . In some implementations, the ISP/stitch engine  2122  is a standalone integrated circuit or processor chip. In some implementations, the ISP/stitch engine  2122  includes two or more hardware chips (such as an ISP and a dedicated stitching IC) configured to perform image processing and stitching operations in tandem. 
     Although the architecture  2100  of  FIG.  21    only includes two image sensors (and although the stitching embodiments described herein are limited to embodiments in which two images are captured and stitched), it should be noted that the principles described herein equally apply to embodiments in which more than two images are captured and stitched. For example, in some implementations, the architecture  2100  can include six cameras arranged in a cubic camera array (e.g., as illustrated in  FIG.  1    and  FIG.  24 A ). In some implementations, the image sensors  2110 A and  2110 B face in substantially opposite directions (e.g., as in image capture apparatus  300  of  FIG.  3   ) such that one or more portions of the boundaries of image A overlap with one or more portions of the boundaries of image B, and such that the collective field of view of both image sensors  2110 A and  2110 B is substantially spherical. 
     In some implementations, the memory  2116  and the memory  2126  may be the same memory. In some implementations, the memories  2116  and  2126  are separate memories. In some implementations, the memory  2116  includes a first memory in which the image sensor  2110 A writes image A, and includes a separate second memory in which the image sensor  2110 B writes image B. In some implementations, one or both of memory  2116  and memory  2126  is located externally to the camera systems in the architecture  2100 . 
     The ISP/stitch engine  2122  may be configured to read the raw image A from the memory  2116 , and perform one or more image processing operations on the raw image A. For example, the ISP/stitch engine  2122  can apply a warp operation to the raw image A selected to convert the overlapping fields of view of the image sensors  2110 A and  2110 B and the resulting fields of view of image A and image B into a single two dimensional stitched image representative of the overlapping fields of view. The ISP/stitch engine  2122  may then write the processed image A to the memory  2126 . 
     The ISP/stitch engine  2122  may be configured to then read the raw image B from the memory  2116 , and perform one or more image processing operations on the raw image B. As noted above, one such processing operation is the warp operation applied to covert overlapping fields of view into a two dimensional image. The ISP/stitch engine  2122  may then write the portion of the processed image B that does not overlap with any portion of the processed image A to the memory  2126 . 
       FIG.  22    illustrates overlapping images captured with multiple image sensors. In the example of  FIG.  22   , image A includes the image portion  2202  and the image portion  2204 , and image B includes the image portion  2204  and the image portion  2206 . For example, the image portion  2204  may be representative of the overlapping fields of view of the image sensor  2110 A and the image sensor  2110 B. 
     Returning to the description of  FIG.  21   , the ISP/stitch engine  2122 , after processing raw image B, writes only the portion of the processed image B that does not overlap with any portion of the processed image A (image portion  2206 ) to the memory  2126 . The ISP/stitch engine  2122  maintains (e.g., in an internal buffer) the portion of the processed image B that does overlap with processed image A (image portion  2204 ). It should be noted that although reference is made to portions of the processed image that overlap with other portions, in practice, the portion of processed image that are accessed for use in stitching operations can extend beyond the boundaries of the portions of the images representative of an overlapping field of view (e.g., by one or two pixels or more) to better enable the performance of the stitching operations. 
     The ISP/stitch engine  2122  then accesses the portion of processed image A that does overlap with processed image B (image portion  2204 ), and combines or blends (1) the maintained portion of the processed image B that does overlap with processed image A, and (2) the accessed portion of processed image A that does overlap with processed image B. Examples of such blending operations include averaging pixel values (e.g., chroma or luma values), smooth pixel values based on the values of neighboring pixels, or any other suitable stitching operation. The ISP/stitch engine  2122  then writes the combined or blended overlapping portions of processed image A and processed image B to the memory  2126  (e.g., within the location in which the accessed portion of processed image A that does overlap with processed image B was stored). The resulting image stored in the memory  2126  is a stitched representation of processed image A and processed image B. 
     The ISP/stitch engine  2122  can identify overlapping portions of images using any suitable method. In some implementations, the overlapping portions of the fields of view of the image sensors  2110 A and  2110 B are predetermined or known in advance. For example, in implementations in which the image sensors  2110 A and  2110 B are secured within a housing or frame such that the image sensors  2110 A and  2110 B do not move relative to each other, the locations of portions of images representative of the overlapping portions of the fields of view of the image sensors can be determined and stored, and the ISP/stitch engine  2122  can access the stored locations of the images representative of the overlapping portions of the fields of view of the image sensors to determine which portions of the processed images should be maintained for combination/blending, and to determine which portions of the process images are not representative of overlapping fields of view (and thus can be written to the memory  2126 ). 
     In some implementations, the ISP/stitch engine  2122  can perform one or more preprocessing operations on the captured images to determine the portions of the images that are representative of overlapping fields of view. For example, the ISP/stitch engine  2122  can perform edge detection, texture analysis, color analysis, depth analysis, and/or any other suitable operations in order to identify the portions of the images representative of the same field of view. In some implementations, a controller or pre-processor (not shown in  FIG.  21   ) can perform one or more pre-processing operations on the raw image data to identify the portions of the images that are representative of overlapping fields of view. For example, a controller or preprocessor can perform the pre-processing operations before image A and image B are written to the memory  2116 . In such implementations, one or more controllers or pre-processors can be coupled between the image sensors  2110 A and  2110 B and the memory  2116 . In some implementations, a controller or pre-processor can be coupled to the memory  2116 , can access the raw image A or raw image B from the memory  2116 , can perform one or more pre-processing operations on the images to determine the locations of the images representative of overlapping fields of view, and can provide the determined locations to the ISP/stitch engine  2122  for use in stitching the images together. 
       FIG.  23    illustrates an example of a technique  2300  for stitching images captured with multiple image sensors. A first image and a second image with overlapping fields of view are captured  2302 . The captured images are stored  2304  in memory. A first of the images is accessed and processed  2306 , and stored  2308  in memory. A second of the images is accessed and processed  2310 , and portions of the processed second image that do not overlap with the processed first image are stored  2312  in memory. The portions of the processed first image that do overlap with portions of the second processed image are accessed  2314  from memory. The overlapping portions of the processed first image and the processed second image are combined  2316 , and the location in the memory storing the portion of the processed first image that overlaps with the processed second image is overwritten  2318  with the combined portions of the processed first image and the processed second image. 
     The architecture  2100  of  FIG.  21    beneficially reduces the required bandwidth to perform image stitching operations relative to conventional stitching systems. For example, in some conventional systems, stitching two images together requires capturing and processing both images, storing both processed images completely to memory, accessing both stored processed images from memory, stitching the processed images together, and storing the stitched image to memory. In contrast, the architecture  2100  may reduce the amount of processed image data that is read from memory to the portion of the processed image A that overlaps with processed image B. The remaining portions of processed image A, and the entirety of processed image B are written to memory only once during the course of stitching images A and B together. 
       FIG.  24 A  illustrates images  2400  captured with a cubic array of image sensors. In the example of  FIG.  24 A , a first camera A faces the viewer, a second camera B faces downward, a third camera C faces leftward, a fourth camera D faces rightward, a fifth camera E faces upward, and a sixth camera F faces away from the viewer. In some implementations, each of the six cameras capturing the images  2400  is a separate modular camera plugged into a cubic camera array housing. In some implementations, the cubic array of cameras used to capture the images  2400  is a singular camera system with six capture mechanisms (e.g., image sensors, lenses, etc.). 
     Prior to the stitching of images  2400  captured by the cubic array of  FIG.  24 A , the images may be stored in a two dimensional image grid.  FIG.  24 B  illustrates a two dimensional grid of images  2450  captured by a cubic array of image sensors. In the example of  FIG.  24 B , the grid of images  2450  is two images tall by three images wide. In order to improve the performance of stitching the images within the grid of  FIG.  24 B  together, the images  2400  captured by the cameras of the array of  FIG.  24 A  can be stored within the grid of images  2450  of  FIG.  24 B  in a particular order. 
     In the top left of the grid of images  2450  of  FIG.  24 B , the image A  2452  captured by the camera A of  FIG.  24 A  is stored. Likewise, in the top middle of the grid of images  2450  of  FIG.  24 B , the image D  2454  captured by camera D is stored, and in the top right of the grid, the image F  2456  captured by camera F is stored. In the bottom row of the grid of images  2450  of  FIG.  24 B , the image B  2458 , the image C  2460 , and the image E  2462  (captured respectively by the cameras B, C, and E of the array of  FIG.  24 A ) are stored, in order. In some implementations, the grid of images  2450  of  FIG.  24 B  is stored as a single image file, composed of images A through F ( 2452 - 2462 ), prior to the stitching together of the images. For example, an image stitching engine can access the image file, divide the image file into two components (a top component including images A, D, and F and a bottom component including images B, C, and E), and can perform two groups of stitching operations. The first group of stitching operations includes: 
     1. Stitching image A  2452  to image D  2454  to produce stitched image AD; 
     2. Stitching stitched image AD to image F  2456  to produce stitched image ADF; 
     3. Stitching image B  2458  to image C  2460  to produce stitched image BC; and 
     4. Stitching stitched image BC to image E  2462  to produce stitched image BCE. 
     The second group of stitching operations can include: 
     1. Stitching an edge of stitched image ADF associated with image A  2452  to an edge of stitched image BCE associated with image B  2458 ; 
     2. Stitching an edge of stitched image ADF associated with image A  2452  to an edge of stitched image BCE associated with image C  2460 ; 
     3. Stitching an edge of stitched image ADF associated with image A  2452  to an edge of stitched image BCE associated with image E  2462 ; 
     4. Stitching an edge of stitched image ADF associated with image D  2454  to an edge of stitched image BCE associated with image B  2458 ; 
     5. Stitching an edge of stitched image ADF associated with image D  2454  to an edge of stitched image BCE associated with image E  2462 ; 
     6. Stitching an edge of stitched image ADF associated with image F  2456  to an edge of stitched image BCE associated with image B  2458 ; 
     7. Stitching an edge of stitched image ADF associated with image F  2456  to an edge of stitched image BCE associated with image C  2460 ; and 
     8. Stitching an edge of stitched image ADF associated with image F  2456  to an edge of stitched image BCE associated with image E  2462 . 
     The end result of the two groups of stitching operations may be a spherical image including image data representative of the collective fields of view of cameras A, B, C, D, E, and F. 
     By storing the images  2400  captured by the cameras of the array of  FIG.  24 A  as illustrated in the grid of images  2450  of  FIG.  24 B , four borders between images (the border between image A  2452  and image D  2454 , the border between image D  2454  and image F  2456 , the border between image B  2458  and image C  2460 , and the border between image C  2460  and image E  2462 ) are representative of overlapping fields of view between adjacent cameras in the array of  FIG.  24 A . Such borders can beneficially improve the performance of stitching operations, as two images associated with such a border do not need to be rotated prior to the performance of the stitching operations. The grid of images  2450  of  FIG.  24 B  can thus be stored in a memory (e.g., within a device housing the camera array of  FIG.  24 A  or within an external memory) prior to the performance of stitching operations such that when the stitching operations are performed, the performance of the stitching operations is improved. 
     Where certain elements of these implementations may be partially or fully implemented using known components, those portions of such known components that are necessary for an understanding of the present disclosure have been described, and detailed descriptions of other portions of such known components have been omitted so as not to obscure the disclosure. 
     In the present specification, an implementation showing a singular component should not be considered limiting; rather, the disclosure is intended to encompass other implementations including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. 
     Further, the present disclosure encompasses present and future known equivalents to the components referred to herein by way of illustration. 
     As used herein, the term “bus” is meant generally to denote any type of interconnection or communication architecture that may be used to communicate data between two or more entities. The “bus” could be optical, wireless, infrared or another type of communication medium. The exact topology of the bus could be, for example, standard “bus,” hierarchical bus, network-on-chip, address-event-representation (AER) connection, or other type of communication topology used for accessing, e.g., different memories in a system. 
     As used herein, the terms “computer,” “computing device,” and “computerized device” include, but are not limited to, personal computers (PCs) and minicomputers (whether desktop, laptop, or otherwise), mainframe computers, workstations, servers, personal digital assistants (PDAs), handheld computers, embedded computers, programmable logic devices, personal communicators, tablet computers, portable navigation aids, Java 2 Platform, Micro Edition (J2ME) equipped devices, cellular telephones, smart phones, personal integrated communication or entertainment devices, or literally any other device capable of executing a set of instructions. 
     As used herein, the term “computer program” or “software” is meant to include any sequence of human or machine cognizable steps which perform a function. Such program may be rendered in virtually any programming language or environment including, for example, C/C++, C#, Fortran, COBOL, MATLAB™, PASCAL, Python, assembly language, markup languages (e.g., HTML, Standard Generalized Markup Language (SGML), XML, Voice Markup Language (VoxML)), as well as object-oriented environments such as the Common Object Request Broker Architecture (CORBA), Java™ (including J2ME, Java Beans), and/or Binary Runtime Environment (e.g., Binary Runtime Environment for Wireless (BREW)). 
     As used herein, the terms “connection,” “link,” “transmission channel,” “delay line,” and “wireless” mean a causal link between any two or more entities (whether physical or logical/virtual) which enables information exchange between the entities. 
     As used herein, the terms “integrated circuit,” “chip,” and “IC” are meant to refer to an electronic circuit manufactured by the patterned diffusion of trace elements into the surface of a thin substrate of semiconductor material. By way of non-limiting example, integrated circuits may include field programmable gate arrays (e.g., FPGAs), a programmable logic device (PLD), reconfigurable computer fabrics (RCFs), systems on a chip (SoC), application-specific integrated circuits (ASICs), and/or other types of integrated circuits. 
     As used herein, the term “memory” includes any type of integrated circuit or other storage device adapted for storing digital data, including, without limitation, read-only memory (ROM), programmable ROM (PROM), electrically erasable PROM (EEPROM), dynamic random access memory (DRAM), Mobile DRAM, synchronous DRAM (SDRAM), Double Data Rate  2  (DDR/ 2 ) SDRAM, extended data out (EDO)/fast page mode (FPM), reduced latency DRAM (RLDRAM), static RAM (SRAM), “flash” memory (e.g., NAND/NOR), memristor memory, and pseudo SRAM (PSRAM). 
     As used herein, the terms “microprocessor” and “digital processor” are meant generally to include digital processing devices. By way of non-limiting example, digital processing devices may include one or more of digital signal processors (DSPs), reduced instruction set computers (RISC), general-purpose complex instruction set computing (CISC) processors, microprocessors, gate arrays (e.g., field programmable gate arrays (FPGAs)), PLDs, reconfigurable computer fabrics (RCFs), array processors, secure microprocessors, application-specific integrated circuits (ASICs), and/or other digital processing devices. Such digital processors may be contained on a single unitary IC die, or distributed across multiple components. 
     As used herein, the term “network interface” refers to any signal, data, and/or software interface with a component, network, and/or process. By way of non-limiting example, a network interface may include one or more of FireWire (e.g., FW400, FW110, and/or other variations), USB (e.g., USB2), Ethernet (e.g., 10/100, 10/100/1000 (Gigabit Ethernet), 10-Gig-E, and/or other Ethernet implementations), MoCA, Coaxsys (e.g., TVnet™), radio frequency tuner (e.g., in-band or out-of-band, cable modem, and/or other radio frequency tuner protocol interfaces), Wi-Fi (802.11), WiMAX (802.16), personal area network (PAN) (e.g., 802.15), cellular (e.g., 3G, LTE/LTE-A/TD-LTE, GSM, and/or other cellular technology), IrDA families, and/or other network interfaces. 
     As used herein, the term “Wi-Fi” includes one or more of IEEE-Std. 802.11, variants of IEEE-Std. 802.11, standards related to IEEE-Std. 802.11 (e.g., 802.11 a/b/g/n/s/v), and/or other wireless standards. 
     As used herein, the term “wireless” means any wireless signal, data, communication, and/or other wireless interface. By way of non-limiting example, a wireless interface may include one or more of Wi-Fi, Bluetooth, 3G (3GPP/3GPP2), High Speed Downlink Packet Access/High Speed Uplink Packet Access (HSDPA/HSUPA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA)(e.g., IS-95A, Wideband CDMA (WCDMA), and/or other wireless technology), Frequency Hopping Spread Spectrum (FHSS), Direct Sequence Spread Spectrum (DSSS), Global System for Mobile communications (GSM), PAN/802.15, WiMAX (802.16), 802.20, narrowband/Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiplex (OFDM), Personal Communication Service (PCS)/Digital Cellular System (DCS), LTE/LTE-Advanced (LTE-A)/Time Division LTE (TD-LTE), analog cellular, cellular Digital Packet Data (CDPD), satellite systems, millimeter wave or microwave systems, acoustic, infrared (i.e., IrDA), and/or other wireless interfaces. 
     As used herein, the term “robot” may be used to describe an autonomous device, autonomous vehicle, computer, artificial intelligence (AI) agent, surveillance system or device, control system or device, and/or other computerized device capable of autonomous operation. 
     As used herein, the terms “camera,” or variations thereof, and “image capture device,” or variations thereof, may be used to refer to any imaging device or sensor configured to capture, record, and/or convey still and/or video imagery which may be sensitive to visible parts of the electromagnetic spectrum, invisible parts of the electromagnetic spectrum (e.g., infrared, ultraviolet), and/or other energy (e.g., pressure waves). 
     While certain aspects of the technology are described in terms of a specific sequence of steps of a method, these descriptions are illustrative of the broader methods of the disclosure and may be modified by the particular application. Certain steps may be rendered unnecessary or optional under certain circumstances. Additionally, certain steps or functionality may be added to the disclosed implementations, or the order of performance of two or more steps may be permuted. All such variations are considered to be encompassed within the disclosure. 
     While the above-detailed description has shown, described, and pointed out novel features of the disclosure as applied to various implementations, it will be understood that various omissions, substitutions, and changes in the form and details of the devices or processes illustrated may be made by those skilled in the art without departing from the disclosure. The foregoing description is in no way meant to be limiting, but rather should be taken as illustrative of the general principles of the technology.