An image processing apparatus acquires a color image including a plurality of primary color signals, performs color space conversion processing on the color image to generate a brightness signal image and a color difference signal image, performs super-resolution processing on the brightness signal image and the color difference signal image to generate a prediction brightness signal image, and uses the prediction brightness signal image to generate a super-resolution color image in which a resolution of the color image is increased.

An image processing apparatus acquires a color image including a plurality of primary color signals, performs color space conversion processing on the color image to generate a brightness signal image and a color difference signal image, performs super-resolution processing on the brightness signal image and the color difference signal image to generate a prediction brightness signal image, and uses the prediction brightness signal image to generate a super-resolution color image in which a resolution of the color image is increased.

A method for color restoration in images includes accessing an image of an object and processing the image based on an image processing operation to provide a processed image, where the image processing affects color of the object. The method further includes determining color adjustment parameters using a trained neural network, wherein an input to the trained neural network is based on the image and the processed image, restoring color in the processed image based on the color adjustment parameters to produce a color-restored image, and displaying the color-restored image on a display device.

A method for color restoration in images includes accessing an image of an object and processing the image based on an image processing operation to provide a processed image, where the image processing affects color of the object. The method further includes determining color adjustment parameters using a trained neural network, wherein an input to the trained neural network is based on the image and the processed image, restoring color in the processed image based on the color adjustment parameters to produce a color-restored image, and displaying the color-restored image on a display device.

A server device includes: a reception unit configured to receive a RAW image from a terminal; and an image pipeline processing unit configured to develop the RAW image, wherein the image pipeline processing unit has: a first image processing unit configured to performing one or more image processes including at least pixel value adjustment on the RAW image and outputting an image-processed RAW image; and a second image processing unit configured to perform one or more image processes including at least demosaic on the RAW image image-processed by the first image processing unit and outputting a developed image.

A server device includes: a reception unit configured to receive a RAW image from a terminal; and an image pipeline processing unit configured to develop the RAW image, wherein the image pipeline processing unit has: a first image processing unit configured to performing one or more image processes including at least pixel value adjustment on the RAW image and outputting an image-processed RAW image; and a second image processing unit configured to perform one or more image processes including at least demosaic on the RAW image image-processed by the first image processing unit and outputting a developed image.

An image processing device includes: a first color conversion processor configured to perform first color conversion processing on image data; a second color conversion processor configured to perform second color conversion processing on the image data; a coefficient calculator configured to calculate a coefficient indicating a blend ratio between an output of the first color conversion processor and an output of the second color conversion processor; and a blend processor configured to blend the output of the first color conversion processor and the output of the second color conversion processor based on the ratio indicated by the coefficient, wherein the coefficient calculator calculates a position on an xy chromaticity diagram of a color indicated by each pixel in image data processed by the first color conversion processor to determine the coefficient based on a distance from the calculated position to a boundary of a color gamut.

Disclosed is a color transform method performed by an electronic device that includes generating an initial color transformed image by performing color transform on a raw image, determine noise amplification degrees indicating degrees to which noise included in the raw image is amplified by the color transform, processing the determined noise amplification degrees, and generating a final color transformed image by filtering the generated initial color transformed image using the processed noise amplification degrees and at least one of the raw image or luminance information of the raw image.

A method performed by a server can include sending a first webpage to a first computing device, the first computing device including a camera, the first webpage including an image-capturing function and including an instruction for a user to obtain a second webpage via a second computing device, the second webpage including a calibration image file; receiving, from the first computing device, multiple captured images that were captured by the camera, the multiple calibration images including instances of a calibration image presented by a display included in the second computing device, the calibration image being a representation of the calibration image file; and based on the multiple captured images, calibrating the camera.

In various examples, image processing techniques are presented for reducing artifacts in images for autonomous or semi-autonomous systems and applications. Systems and methods are disclosed for deferring at least a portion of a strength associated with a color correction matrix (CCM) of an image processing pipeline to one or more other stages associated with the image processing pipeline. For instance, the CCM is used to determine a first CCM and a second, deferred CCM. The first CCM may be associated with an earlier stage of the image processing pipeline while the deferred CCM is associated with a later stage of the image processing pipeline. In some examples, the deferred CCM is combined with another matrix, such as a color space conversion matrix. By deferring part of the CCM, the image processing pipeline may reduce a number of artifacts and/or eliminate the artifacts that occur when processing image data.