Abstract: Flare compensation includes obtaining a first input frame, which includes lens flare, and a second input frame; obtaining a stitch line between the first input frame and the second input frame; obtaining, for the first input frame, a first intensity profile; obtaining, for the second input frame, a second intensity profile; obtaining an intensity differences profile; obtaining a first dark corner profile based on a first illumination of a first area outside a first image circle of the first input frame; obtaining a second dark corner profile based on a second illumination of a second area outside a second image circle of the second input frame; obtaining a dark corner difference profile; calculating a flare model; obtaining a flare mask; and correcting the first input frame by subtracting the flare mask from the first input frame.

Abstract: Image signal processing including generating image signal processing based encoding hints for motion estimation may include an image signal processor obtaining an input image portion of an input image from the input image signal, generating motion information for the input image portion, processing the input image portion based on the motion information, outputting processed image data, and outputting the motion information as encoding hints, such that the motion information is accessible by an encoder for generating an encoded output bitstream by obtaining the processed image data as source image data, obtaining the motion information, generating prediction data for encoding the source image data based on the motion information, generating encoded image data based on the prediction data, and including the encoded image data in an encoded output bitstream.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving a first image from a first image sensor; receiving a second image from a second image sensor; stitching the first image and the second image to obtain a stitched image; identifying an image portion of the stitched image that is positioned on a stitching boundary of the stitched image; and inputting the image portion to a machine learning module to obtain a score, wherein the machine learning module has been trained using training data that included image portions labeled to reflect an absence of stitching and image portions labeled to reflect a presence of stitching, wherein the image portions labeled to reflect a presence of stitching included stitching.

Abstract: Systems and methods are disclosed for generating long exposure images for high dynamic range processing. For example, methods may include receiving a short exposure image that was captured using an image sensor; receiving an additional image that was captured using the image sensor; determining a long exposure image based on the short exposure image and the additional image; applying high dynamic range processing to the short exposure image and the long exposure image to obtain an output image with a larger dynamic range than the short exposure image; and transmitting, storing, or displaying an image based on the output image.

Abstract: Systems and methods are disclosed for high dynamic rate processing based on angular rate measurements. For example, methods may include receiving a short exposure image that was captured using an image sensor; receiving a long exposure image that was captured using the image sensor; receiving an angular rate measurement captured using an angular rate sensor attached to the image sensor during exposure of the long exposure image; determining, based on the angular rate measurement, whether to apply high dynamic range processing to an image portion of the short exposure image and the long exposure image; and responsive to a determination not to apply high dynamic range processing to the image portion, selecting the image portion of the short exposure image for use as the image portion of an output image and discard the image portion of the long exposure image.

Abstract: Systems and methods are disclosed for generating long exposure images for high dynamic range processing. For example, methods may include receiving a short exposure image that was captured using an image sensor; receiving an additional image that was captured using the image sensor; determining a long exposure image based on the short exposure image and the additional image; applying high dynamic range processing to the short exposure image and the long exposure image to obtain an output image with a larger dynamic range than the short exposure image; and transmitting, storing, or displaying an image based on the output image.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving an image from an image sensor; applying a filter to the image to obtain a low-frequency component image and a high-frequency component image; determining a first enhanced image based on a weighted sum of the low-frequency component image and the high-frequency component image, where the high-frequency component image is weighted more than the low-frequency component image; determining a second enhanced image based on the first enhanced image and a tone mapping; and storing, displaying, or transmitting an output image based on the second enhanced image.

Abstract: Systems and methods are disclosed for image capture. For example, methods may include accessing a sequence of images from an image sensor; determining a sequence of parameters for respective images in the sequence of images based on the respective images; storing the sequence of images in a buffer; determining a temporally smoothed parameter for a current image in the sequence of images based on the sequence of parameters, wherein the sequence of parameters includes parameters for images in the sequence of images that were captured after the current image; applying image processing to the current image based on the temporally smoothed parameter to obtain a processed image; and storing, displaying, or transmitting an output image based on the processed image.

Abstract: Systems and methods are disclosed for non-local means denoising of images. For example, methods may include receiving an image from an image sensor; determining a set of non-local means weights for the image; applying the set of non-local means weights to the image to obtain a first denoised image; applying the set of non-local means weights to the first denoised image to obtain a second denoised image; and storing, displaying, or transmitting an output image based on the second denoised image.

Abstract: Multiple lookup tables (LUTs) storing different numbers of control point values are used to process pixels within different blocks of an image, such as after image processing using tone mapping and/or tone control, and/or to collect histogram information or implement 3D LUTs. First control point values stored within a first LUT are applied against pixels of a given block of an image to produce a distorted image block. Second control point values stored within a second lookup table are applied against a pixel of the distorted image block to produce a processed pixel. The second LUT is one of a plurality of second LUTs and stores fewer values than the first LUT. A processed image is produced using the processed pixel. The processed image is then output for further processing or display.

Abstract: Image signal processing may include flare reduction, which may include obtaining a first input frame captured by a first image capture device of an image capture apparatus, the first image capture device having a first field-of-view, and the first input frame including lens flare corresponding to a primary light source; obtaining a second input frame captured by a second image capture device of the image capture apparatus, the second image capture device having a second field-of-view partially overlapping the first field-of-view; obtaining primary light source information corresponding to the primary light source based on the first input frame and the second input frame; obtaining a processed frame by modifying the first input frame based on the primary light source information to minimize the lens flare; and outputting the processed frame.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving a first image from a first image sensor; receiving a second image from a second image sensor; stitching the first image and the second image to obtain a stitched image; identifying an image portion of the stitched image that is positioned on a stitching boundary of the stitched image; and inputting the image portion to a machine learning module to obtain a score, wherein the machine learning module has been trained using training data that included image portions labeled to reflect an absence of stitching and image portions labeled to reflect a presence of stitching, wherein the image portions labeled to reflect a presence of stitching included stitching.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving a first image from a first image sensor; receiving a second image from a second image sensor; determining an electronic rolling shutter correction mapping for the first image and the second image; determining a parallax correction mapping based on the first image and the second image for stitching the first image and the second image; determining a warp mapping based on the parallax correction mapping and the electronic rolling shutter correction mapping, wherein the warp mapping applies the electronic rolling shutter correction mapping after the parallax correction mapping; applying the warp mapping to image data based on the first image and the second image to obtain a composite image; and storing, displaying, or transmitting an output image that is based on the composite image.

Abstract: Systems and methods are disclosed for high dynamic rate processing based on angular rate measurements. For example, methods may include receiving a short exposure image that was captured using an image sensor; receiving a long exposure image that was captured using the image sensor; receiving an angular rate measurement captured using an angular rate sensor attached to the image sensor during exposure of the long exposure image; determining, based on the angular rate measurement, whether to apply high dynamic range processing to an image portion of the short exposure image and the long exposure image; and responsive to a determination not to apply high dynamic range processing to the image portion, selecting the image portion of the short exposure image for use as the image portion of an output image and discard the image portion of the long exposure image.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving an image from an image sensor; applying a filter to the image to obtain a low-frequency component image and a high-frequency component image; determining a first enhanced image based on a weighted sum of the low-frequency component image and the high-frequency component image, where the high-frequency component image is weighted more than the low-frequency component image; determining a second enhanced image based on the first enhanced image and a tone mapping; and storing, displaying, or transmitting an output image based on the second enhanced image.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving, by an image signal processor, one or more input image signals from one or more image sensors; determining a mapping based on the input image signal(s), wherein the mapping includes records that associate image portions of an output image with corresponding image portions of the input image signal(s); sorting the records of the mapping according to an order of the corresponding image portions of the input image signal(s); applying, by the image signal processor, image processing to image portions of the input image signal(s) to determine image portions of one or more processed images in the order; and determining, by the image signal processor, the image portions of the output image based at least in part on the mapping and the corresponding image portions of the processed image(s) in the order.

Abstract: A system accesses an image with each pixel of the image having luminance values each representative of a color component of the pixel. The system generates a first histogram for aggregate luminance values of the image, and accesses a target histogram for the image representative of a desired global image contrast. The system computes a transfer function based on the first histogram and the target histogram such that when the transfer function is applied, a histogram of the modified aggregate luminance values is within a threshold similarity of the target histogram. The system modifies the image by applying the transfer function to the luminance values of the image to produce a tone mapped image, and outputs the modified image.

Abstract: Image signal processing based encoding hints for bitrate control may include an image signal processor receiving an input image signal, identifying a block of a frame based on the input image signal, generating processed image data corresponding to the block, determining a processed image indication for the block, and outputting the processed image data and the processed image indication as an encoding hint. Image signal processing based encoding hints for bitrate control may include an encoder receiving source image data for a frame, the source image data including the processed image data corresponding to the block, receiving source image indications for the frame, the source image indications including the processed image indication, determining an encoding parameter for encoding the frame based on the processed image indication, generating an encoded frame by encoding the frame using the encoding parameter, and outputting or storing the encoded frame.

Abstract: Image signal processing may include flare reduction, which may include obtaining a first input frame captured by a first image capture device of an image capture apparatus, the first image capture device having a first field-of-view, and the first input frame including lens flare corresponding to a primary light source; obtaining a second input frame captured by a second image capture device of the image capture apparatus, the second image capture device having a second field-of-view partially overlapping the first field-of-view; obtaining primary light source information corresponding to the primary light source based on the first input frame and the second input frame; obtaining a processed frame by modifying the first input frame based on the primary light source information to minimize the lens flare; and outputting the processed frame.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving a first image from a first image sensor; receiving a second image from a second image sensor; determining an electronic rolling shutter correction mapping for the first image and the second image; determining a parallax correction mapping based on the first image and the second image for stitching the first image and the second image; determining a warp mapping based on the parallax correction mapping and the electronic rolling shutter correction mapping, wherein the warp mapping applies the electronic rolling shutter correction mapping after the parallax correction mapping; applying the warp mapping to image data based on the first image and the second image to obtain a composite image; and storing, displaying, or transmitting an output image that is based on the composite image.