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, 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: 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 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 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: Image signal processing may include desaturation control, which may include adaptive desaturation control. Image signal processing with desaturation control may include obtaining, by an image signal processor, from an image sensor, an input image signal representing an input image, obtaining, by the image signal processor, color correction information for the input image, obtaining a color corrected image based on the input image using color correction with desaturation control such that inaccurate colorization of the color corrected image is minimized, and outputting the color corrected 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 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 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: Image signal processing may include obtaining a first portion of a first input image, the first portion having a defined width, a defined height, and a defined portion size, which is a product of multiplying the defined width by the defined height, the first portion of the first input image including a first set of image elements in raster order and having a cardinality of the defined portion size. Image signal processing may include sequential in-place blocking transposition of the first input image, which may include using a buffer, omit using another buffer, and has linear complexity, and may include buffering the first set of image elements using the buffer, the buffer having a defined buffer size within twice the defined portion size, and outputting the first set of image elements from the buffer in block order.

Abstract: Obtaining color adjusted image portions using sub-three-dimensional look-up tables may include obtaining the color adjusted image portion by obtaining a value for an input image portion wherein the value includes a first parameter, a second parameter, and a third parameter, obtaining a color adjusted first parameter from at least a first one-dimensional look-up table based on the first parameter, the second parameter, and the third parameter, obtaining a color adjusted second parameter from at least a second one-dimensional look-up table based on the first parameter, the second parameter, and the third parameter, obtaining a color adjusted third parameter from at least a third one-dimensional look-up table based on the first parameter, the second parameter, and the third parameter, and outputting the color adjusted image portion based on a combination of the color adjusted first parameter, the color adjusted second parameter, and the color adjusted third parameter.

Abstract: A system access a reference frame and temporally adjacent frames. For each portion of the reference image frame, the system calculates a pixel distance value between the portion of the reference image frame and a corresponding portion of each temporally adjacent image frame. If the pixel distance value indicates a potential ghosting artifact, the system computes a set of spatial noise reduction values for the image portion. Otherwise, the system computes a set of temporal noise reduction values for the image portion. The system blends the sets of computed spatial noise reduction values and the sets of computed temporal noise reduction values, and generates a modified reference image frame based on the blended set of noise reduction values.

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: An offset blade of a propeller varies the blade pitch according to in-flight conditions in order to maximize the performance and efficiency of the propeller. As the propeller begins to rotate, upward thrust forces and lateral centrifugal forces are generated on the blade. The balance between the thrust and centrifugal forces determines the blade pitch. At rest, the blade pitch is minimized whereas when in-flight, the blade pitch of the propeller blade is maximized. The blade is coupled with a rotor hub through a connector that determines the maximum and minimum blade pitches of the blade. Furthermore, the connector reduces blade pitch oscillations that often occur in flight.

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: 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 determines for each color channel of each portion of the image, a corresponding adjustment value to apply to the color channel to correct for a color irregularity. The system determines a corrected adjustment value based on a difference between twice the pixel value and the maximum saturation value. If the adjustment value as applied is larger than the corrected adjustment value, the system applies the adjustment value to the corresponding color channel of the image portion to produce the adjusted color channel. Otherwise, the system applies the corrected adjustment to the corresponding color channel of the image portion to produce an adjusted color channel. The system generates a modified image based on the adjusted color channel.

Abstract: A system identifies a scaling position in a captured image, and identifies red subpixels adjacent to the scaling position. The system computes a scaled red subpixel for the scaling position based on the identified red subpixels according to constraints. The system further computes a scaled blue subpixel based on identified adjacent blue subpixels, according to constraints, and computes a scaled green subpixel position based on Gr and Gb subpixels adjacent to the scaling position according to certain constraints. The system then generates a scaled image representative of the captured image, the scaled image including at least the scaled red subpixel value, the scaled blue subpixel value, and the scaled green subpixel value.

Abstract: Generating a calibrated camera alignment model for image capture devices having overlapping fields-of-view may include identifying a camera alignment model describing a first candidate alignment path for a defined location in a first input frame and a second candidate alignment path for the defined location in a second input frame, identifying a third candidate alignment path spatially adjacent to the first candidate alignment path, identifying a fourth candidate alignment path spatially adjacent to the second candidate alignment path, identifying a first point along the first candidate alignment path or the second candidate alignment path corresponding to a second point along the third candidate alignment path or the fourth candidate alignment path, and updating the camera alignment model based on the first point, the second point, or both.