Gopro, Inc.

Bruno César Douady-Pleven

Thomas Nicolas Emmanuel Veit

Antoine Meler

Christophe Clienti

Marc Lebrun

Vincent Vacquerie

Guillaume Matthieu Guérin

Nicolas Boehrer

Guillaume Matthieu Guerin

Michael Serge Andre Kraak

Violaine Marie Mong-lan Sudret

Antoine Regimbeau

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; obtaining corrected images based on the first image and the second image; obtaining stabilized images based on the corrected images; applying a parallax correction to the stabilized images to obtain a composite image; obtaining a transformed image from the composite image; and encoding an output image based on the transformed image.

Image stitching with electronic rolling shutter correction

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.

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.

Entropy maximization based auto-exposure

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.

Image signal processing for reducing lens flare

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.

Double non-local means denoising

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.

Temporal smoothing in image capture systems

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.

Desaturation control

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.

Sequential in-place blocking transposition for image signal processing

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.

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.

Local tone mapping

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.

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Bures-sur-Yvette, France

Bruno César Douady-Pleven