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 includes generating an exposure compensated image based on a gain value applied to an exposure level of a first image and a gain value applied to an exposure level of a second image. The gain value may be progressively increased from an approximate center of the first image to an edge of the first image to a common exposure level. The gain value may be progressively decreased from an approximate center of the second image to an edge of the second image to the common exposure level. Gain values may be scaled on each color channel for a pixel based on a saturation level of the pixel.

Abstract: Color correction is integrated within global tone mapping operations to modify an image captured using an image capture device. New luminance values are determined for the pixels of the image by performing global tone mapping against those pixels using one or more sets of color correction values, which are applied against respective luminance values and color components of those pixels. The sets of color correction values are identified within 3×3 color correction matrices. A gain curve for modifying contrast values of the image is determined based on at least one of the new luminance values. A modified image is then generated by modifying the contrast values according to the gain curve, for example, by applying a gamma curve against the contrast values using data stored in a three-dimensional lookup table.

Abstract: Systems and methods are disclosed for non-linear color correction. The method including receiving an input image from an image sensor, converting the input image from a red, green, blue (RGB) color space format to an alternate color space format, determining localized hue correction parameters for a selected color in the alternate color space, determining localized saturation correction parameters for a selected hue in the alternate color space, applying the localized hue correction parameters and the localized saturation correction parameters to the input image to generate an output image and storing, displaying, or transmitting the output image based on at least the localized hue correction parameters and the localized saturation correction parameters.

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: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

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: Systems and methods are disclosed for image signal processing. For example, methods may include accessing an image from an image sensor; detecting an object area on the image; classifying the object area on the image; applying a filter to an object area of 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 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: 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 analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

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 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: Color correction is integrated within global tone mapping operations to modify an image captured using an image capture device. New luminance values are determined for the pixels of the image by performing global tone mapping against those pixels using one or more sets of color correction values, which are applied against respective luminance values and color components of those pixels. The sets of color correction values are identified within 3×3 color correction matrices. A gain curve for modifying contrast values of the image is determined based on at least one of the new luminance values. A modified image is then generated by modifying the contrast values according to the gain curve, for example, by applying a gamma curve against the contrast values using data stored in a three-dimensional lookup table.

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: 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: 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.