Abstract: A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause performance of operations comprising operations to access an image captured by an image sensor, obtain a transfer function for mapping pixel values, determine a faces indication that reflects a proportion of a scene depicted in the image that includes one or more human faces, and modify the transfer function based on the faces indication. Modifying the transfer function based on the faces indication comprises adjusting a gain of the transfer function to move the gain closer to unity. The operations include to apply the transfer function to pixel values of the image to produce a tone mapped image and output the tone mapped image.

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 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; 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 a 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.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may 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; 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 a 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.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may 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; 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 a 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.

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: 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, systems may include an image sensor and a processing apparatus. The image sensor captures image data using a plurality of selectable exposure times. The processing apparatus receives a first image from the image sensor captured with a first exposure time and receives a second image from the image sensor captured with a second exposure time that is less than the first exposure time. A high dynamic range image is determined based on the first image and the second image, wherein 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. An output image that is based on the high dynamic range image is stored, displayed, or transmitted.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may 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; 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 a 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.

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: 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: 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: 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: 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.

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 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.