Gopro, Inc.

Bruno César Douady

Guillaume Matthieu Guérin

Thomas Nicolas Emmanuel Veit

Michael Serge Andre Kraak

Hervé Hornung

Alexis Lefebvre

Vincent Vacquerie

Karl Krissian

Bruno Faure

Eric Chasseur

Thomas Derbanne

Maxim Karpushin

Vadim Polonichko

Sandeep Doshi

Guillaume Matthieu Guerin

Guillaume Matthieu Guèrin

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.

Three-dimensional noise reduction

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.

Global tone mapping

Systems and methods are disclosed for high dynamic range processing based on angular rate measurements. For example, methods may include receiving a short exposure image that was captured using an image sensor; receiving a long exposure image that was captured using the image sensor; receiving an angular rate measurement captured using an angular rate sensor attached to the image sensor during exposure of the long exposure image; determining, based on the angular rate measurement, whether to apply high dynamic range processing to an image portion of the short exposure image and the long exposure image; and responsive to a determination not to apply high dynamic range processing to the image portion, selecting the image portion of the short exposure image for use as the image portion of an output image and discard the image portion of the long exposure image.

High dynamic range processing based on angular rate measurements

Systems and methods are disclosed for circular stitching of images. For example, methods may include accessing a first image captured using a first image sensor; accessing a second image captured using a second image sensor; determining a cost table for a circular stitching boundary that includes overlapping regions of the first image and the second image; determining an extended disparity profile based on a periodic extension of the cost table and a smoothness criterion, wherein the extended disparity profile has a length greater than the width of the cost table; determining a binocular disparity profile of a length equal to the width of the cost table based on a contiguous subsequence of the extended disparity profile; and stitching the first image and the second image using the binocular disparity profile to obtain a combined image.

Circular stitching of images

An image is processed using a combination of techniques, particularly, in which low frequency information is processed using multiple tone control and high frequency information is processed using local tone mapping. An image is divided into a plurality of blocks including a given block. Low frequency information and high frequency information of the given block are separated. The low frequency information is processed using multiple tone control. The high frequency information is processed using local tone mapping. A processed image is then produced based on the processed low frequency information and based on the processed high frequency information, the processed image corresponding to the image captured using the image sensor. The processed image is then output for storage or display. Processing the low frequency information can include using a gain curve and bilinear interpolation. Processing the high frequency information can include using an edge preservation filter.

Tone mapping and tone control integrations for image processing

Image analysis and processing may include a first sensor readout unit receiving first Bayer format image data corresponding to a first input image, a second sensor readout unit receiving second Bayer format image data corresponding to a second input image, a first Bayer-to-RGB unit obtaining first RGB format image data based on the first Bayer format image data, a second Bayer-to-RGB unit obtaining second RGB format image data based on the second Bayer format image data, a high dynamic range unit configured obtaining high dynamic range image data based on a combination of the first RGB format image data and the second RGB format image data, an RGB-to-YUV unit obtaining YUV format image data based on the high dynamic range image data, and a three-dimensional noise reduction unit obtaining processed image data based on the YUV format image data.

Image processing including noise reduction

Flare compensation includes obtaining a dark corner intensity differences profile between a first and a second image based on a relative illumination of an area outside a first image circle of the first image and a second image circle of the second image. The dark corner intensity differences profile is obtained for a luminance (Y) component. A flare profile is obtained using an intensity differences profile and the dark corner intensity differences profile. The intensity differences profile is obtained for the Y component along a stitch line between the first image and the second image. The flare profile of the Y component is converted to an RGB flare profile. The first image is modified based on the RGB flare profile to obtain a processed first image.

Image signal processing for reducing lens flare

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.

Systems and methods are disclosed for high dynamic rate processing based on angular rate measurements. For example, methods may include receiving a short exposure image that was captured using an image sensor; receiving a long exposure image that was captured using the image sensor; receiving an angular rate measurement captured using an angular rate sensor attached to the image sensor during exposure of the long exposure image; determining, based on the angular rate measurement, whether to apply high dynamic range processing to an image portion of the short exposure image and the long exposure image; and responsive to a determination not to apply high dynamic range processing to the image portion, selecting the image portion of the short exposure image for use as the image portion of an output image and discard the image portion of the long exposure image.

Image signal processing includes obtaining two or more image signals from a first image sensor, where each of the two or more image signals has a different exposure and obtaining two or more image signals from a second image sensor, where each of the two or more image signals has a different exposure. 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. Image signal processing further includes processing a transformed base layer of the exposure compensated image to obtain a high dynamic range (HDR) image.

High dynamic range processing on spherical images

An image capture device may include an image sensor, a processor, and memory. The image sensor may be configured to obtain an image. The processor may be configured to: generate a grid on the image forming tiles; determine a fringing level of each vertex of the vertices; sort all of the tiles based on the fringing level of each tile so that the tiles are sorted in a descending order from the tile with a highest of the fringing levels to the tile with a lowest of the fringing levels; and apply a fringing compensation to a subset of the sorted tiles to obtain a processed image. The memory may be configured to store the processed image.

Method and apparatus for partial correction of images

A face located along a stitch line in a spherical image is detected by rendering views of regions of the spherical image along the stitch line. The spherical image may be produced by combining first and second images. A first view of a projection of the spherical image is rendered. A scaling factor for rendering a second view of the projection is determined based characteristics of the first portion of the face. The second view is then rendered according to the scaling factor. The use of the scaling factor to render the second view causes a change in the depiction of the second portion of the face. For example, the scaling factor can indicate to change the resolution or expected size of the second portion of the face when rendering the second view. A face is then detected within the spherical image based on the rendered first and second views.

Face detection in spherical images

Image signal processing includes obtaining two or more image signals from a first hyper-hemispherical image sensor, where each of the two or more image signals has a different exposure and obtaining two or more image signals from a second hyper-hemispherical image sensor, where each of the two or more image signals has a different exposure. 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. Image signal processing further includes performing high dynamic range (HDR) processing on the exposure compensated image. The HDR processing may be performed on a high a frequency portion of the exposure compensated image.

Growing community of inventors

Orsay, France

Bruno César Douady