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: 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: Systems and methods are disclosed for denoising chrominance channels of images. For example, methods may include receiving an image from one or more image sensors; determining a set of weights for the image based on a luminance channel of the image, wherein a weight in the set of weights corresponds to a subject pixel and a candidate pixel and is determined based on luminance values of one or more pixels of the image centered at the subject pixel and one or more pixels of the image centered at the candidate pixel; applying the set of weights to chrominance channels of the image to obtain a denoised image, wherein the subject pixel of the denoised image is determined based on the weight multiplied by the candidate pixel of the image; and storing, displaying, or transmitting an output image based on the denoised 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: A camera mode to use for capturing an image or video is selected by estimating high dynamic range (HDR), motion, and light intensity with respect to a scene of the image or video to capture. An image capture device includes a HDR estimation unit to detect whether HDR is present in a scene of an image to capture, a motion estimation unit to determine whether motion is detected within the scene, and a light intensity estimation unit to determine whether a scene luminance for the scene meets a threshold. A mode selection unit selects a camera mode to use for capturing the image based on output of the HDR estimation unit, the motion estimation unit, and the light intensity estimation unit. An image sensor captures the image according to the selected camera mode.

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 method and apparatus for denoising a video are disclosed. The method includes obtaining multiple frames of the video. One or more frames of the video may be temporally precedent or temporally subsequent to a central frame. The method includes performing a first denoising of the multiple frames. The method includes concatenating the denoised frames into a concatenated input. The method includes performing a second denoising of the concatenated input. The method includes outputting a denoised frame based on the denoised concatenated input.

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: An image capture apparatus may include an image sensor, a motion sensor, and an auto-exposure unit. The auto-exposure unit may obtain an input image captured during an exposure interval and corresponding motion data indicating motion of the image capture apparatus during the exposure interval. The auto-exposure unit may obtain image information-amount data for the input image. The auto-exposure unit may obtain derivative information-amount data based on the information-amount data and a candidate exposure adjustment. The auto-exposure unit may obtain an information-amount maximizing exposure interval based on the information-amount data and the derivative information-amount data. The image capture apparats may control the image sensor to obtain a subsequent input image signal representing a subsequent input image captured during the information-amount maximizing exposure interval, and output or store information representing the subsequent input image.

Abstract: Systems and methods are disclosed for denoising chrominance channels of images. For example, methods may include receiving an image from one or more image sensors; determining a set of weights for the image based on a luminance channel of the image, wherein a weight in the set of weights corresponds to a subject pixel and a candidate pixel and is determined based on luminance values of one or more pixels of the image centered at the subject pixel and one or more pixels of the image centered at the candidate pixel; applying the set of weights to chrominance channels of the image to obtain a denoised image, wherein the subject pixel of the denoised image is determined based on the weight multiplied by the candidate pixel of the image; and storing, displaying, or transmitting an output image based on the denoised image.

Abstract: Implementations disclosed herein include an image capture device, a system, and a method for performing multiscale denoising of an image. An image processor of the image capture device obtains a first image. The first image may be in any format and may include noise artifacts. The image processor decomposes the first image into one or more sub-images. The sub-images may range from a coarse scale to a fine scale. In some implementations, the image processor iteratively denoises each of the one or more sub-images from the coarse scale to the fine scale. The image processor reconstructs the one or more denoised sub-images to produce a denoised image. A memory of the image capture device may be configured to store the denoised image.

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: Implementations disclosed herein include an image capture device, a system, and a method for performing multiscale denoising of a video. An image processor of the image capture device obtains a video frame. The video frame may be in any format and may include noise artifacts. The image processor decomposes the video frame into one or more sub-frames. In some implementations, the image processor denoises each of the one or more sub-frames. The image processor decomposes one or more video frames in a temporal buffer into one or more temporal sub-frames. The image processor denoises each of the temporal sub-frames. The image processor reconstructs the one or more denoised sub-frames and the one or more temporal sub-frames to produce a denoised video frame. A memory of the image capture device may be configured to store the denoised video frame.

Abstract: A method and apparatus for denoising a video are disclosed. The method includes obtaining multiple frames of the video. One or more frames of the video may be temporally precedent or temporally subsequent to a central frame. The method includes performing a first denoising of the multiple frames. The method includes concatenating the denoised frames into a concatenated input. The method includes performing a second denoising of the concatenated input. The method includes outputting a denoised frame based on the denoised concatenated input.

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 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 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: 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: 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.