Abstract: A device comprising an image processor configured to implement: a first machine learning model for performing restoration processing on degraded image data; and a second machine learning model for recognizing areas of an image requiring processing emphasis during the restoration processing, wherein the output of the second machine learning model is an input to the first machine learning model to optimize the restoration processing.

Abstract: The present disclosure provides example apparatuses, methods, and devices for denoising an image. One example apparatus performs operations including receiving an input image. A trained artificial intelligence model is implemented to form an estimate of a noise pattern in the input image and form an output image by subtracting the estimate of the noise pattern from the input image, where the model is configured to form the estimate of the noise pattern, and the estimate of the noise pattern is representative of a noise pattern that is characteristic to a specific image sensor type.

Abstract: An image processor comprising a plurality of modules, the plurality of modules comprising a first module and a second module, wherein the image processor is configured to receive an input image and output a plurality of mathematical descriptors for characteristic regions of the input image, wherein: the first module is configured to implement a first trained artificial intelligence model to detect a set of characteristic regions in the input image; and the second module is configured to implement a second trained artificial intelligence model to determine a mathematical descriptor for each of said set of characteristic regions; wherein the first and second trained artificial intelligence models are collectively trained end to end.

Abstract: Disclosed is an image processing device comprising a processor configured to estimate the scale of image features by the steps of: processing multiple images of a scene by means of a first trained model to identify features in the images and to estimate the depths of those features in the images; processing the multiple images by a second trained model to estimate a scaling for the images; and estimating the scales of the features by adjusting the estimated depths in dependence on the estimated scaling. A method for training an image processing model is also disclosed.

Abstract: An image processor for transforming an input image, the image processor being configured to implement a trained artificial intelligence model, where the image processor is configured to: receive the input image; based on one or both of (i) the content of the input image and (ii) features extracted from the input image, process the image by the trained artificial intelligence model to: (i) determine a set of image filters; and (ii) for each of a plurality of subregions of the image, select an image filter from the set of image filters; and for each of the plurality of subregions of the image, apply the respective image filter to the subregion or to features extracted from that subregion. This may allow for differentiable selection of filters from a discrete learnable and decorrelated group of filters to allow for content based spatial adaptations.

Abstract: An image processing module configured to implement a multi-part trained artificial intelligence model, wherein the image processing module is configured to: receive an input image; implement a first part of the model to determine a first transformation for the image in a first colour space; apply the first transformation to the image to form a first adjusted image; implement a second part of the model to determine a second transformation for the image in a second colour space; apply the second transformation to the first adjusted image to form a second adjusted image; and output an image derived from the second adjusted image.

Abstract: The present disclosure relates to methods and devices for performing depth estimation on image data. In one example, a device performs depth estimation on first and second images captured using one or more cameras having a color filter array. Each image of the first and second images comprises multiple color channels. Each color channel of the multiple color channels corresponds to a respective color channel of the color filter array. The device performs the depth estimation by estimating disparity from the color channels of the first and second images.

Abstract: A processing entity generates a model for estimating scene illumination colour for a source image captured by a camera The processing entity acquires a set of images, captured by a respective camera, the set of images as a whole including images captured by multiple cameras; forms a set of tasks by assigning each image of the images set to a respective task such that images in the same task have in common that a the images are in a predetermined range; trains model parameters by repeatedly: selecting at least one of the tasks, forming an interim set of model parameters based on a first subset of the images of that task, estimating the quality of the interim set of model parameters against a second subset of the images of that task and updating the parameters of the model based on the interim set of parameters and the estimated quality.

Abstract: An image processing system configured to receive an input time-of-flight depth map representing the distance of objects in an image from a camera at a plurality of locations of pixels in the respective image, and in dependence on that map to generate an improved time-of-flight depth map for the image, the input time-of-flight depth map having been generated from at least one correlation image representing the overlap between emitted and reflected light signals at the plurality of locations of pixels at a given phase shift, the system being configured to generate the improved time-of-flight depth map from the input time-of-flight depth map in dependence on a colour representation of the respective image and at least one correlation image.

Abstract: The technology of this application related to an image processor comprising a plurality of modules, the plurality of modules comprising a first module and a second module, wherein the image processor is configured to receive an input image and output a plurality of mathematical descriptors for characteristic regions of the input image. The first module is configured to implement a first trained artificial intelligence model to detect a set of characteristic regions in the input image; and the second module is configured to implement a second trained artificial intelligence model to determine a mathematical descriptor for each of said set of characteristic regions. The first and second trained artificial intelligence models are collectively trained end to end.

Abstract: One example image processing device is provided. The example image processing device can include at least one processor and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to estimate noise in an image, the image being represented by a set of pixels and each pixel of the set of pixels having a value associated with it on each of one or more channels, where estimating the noise comprises processing, using a first trained model that detects stochastic noise, data derived from the image to form a first noise estimate, processing, using a second trained model that detects extreme pixel values, data derived from the image to form a second noise estimate, and combining the first and second noise estimates to form an aggregated noise estimate.

Abstract: An image processor comprising a plurality of processing modules configured to transform a raw image into an output image. The plurality of processing modules comprise a first module and a second module, each of which implements a respective trained artificial intelligence model. The first module is configured to implement an image transformation operation that recovers luminance from the raw image. The second module is configured to implement an image transformation operation that recovers chrominance from the raw image.

Abstract: A device for estimating a scene illumination color for a source image is configured to: determine a set of candidate illuminants and for each of the candidate illuminants, determine a respective correction of the source image; for each of the candidate illuminants, apply the respective correction to the source image to form a corresponding set of corrected images; for each corrected image from the set of corrected images, implement a trained data-driven model to estimate a respective probability of achromaticity of the respective corrected image; and based on the estimated probabilities of achromaticity for the set of corrected images, obtain a final estimate of the scene illumination color for the source image. This approach allows for the evaluation of multiple candidate illuminates to determine an estimate of the scene illumination color.

Abstract: An image processing device comprising a processor configured to generate a refocused image from an input image and an map indicating depth information for the image, by the steps of: for each of a plurality of planes associated with respective depths within the image: generating a depth mask having values indicating whether regions of the input image are within a specified range of the plane, wherein an assessment of whether a region is within the specified range of the plane is made through the evaluation of a differentiable function of the range between regions of the input image and the plane as determined from the map; generating a masked image from the input image and the generated depth mask; refocusing the masked image using a blurring kernel to generate a refocused partial image; and generating the refocussed image from the plurality of refocussed partial images.

Abstract: An image processing apparatus and a method are provided. The apparatus comprises a plurality of processing modules configured to operate in series to refine a raw image captured by a camera, the modules comprising a first module and a second module, each of which independently implements a respective trained artificial intelligence model, wherein: the first module implements an image transformation operation that performs an operation from the set comprising: (i) an essentially pixel-level operation that increases sharpness of an image input to the module, (ii) an essentially pixel-level operation that decreases sharpness of an image input to the module, (iii) an essentially pixel-block-level operation on an image input to the module; and the second module as a whole implements a different operation from the said set.

Abstract: The present disclosure relates to methods and devices for performing depth estimation on image data. In one example, a device performs depth estimation on first and second images captured using one or more cameras having a color filter array. Each, image of the first and second images comprises multiple color channels. Each color channel of the multiple color channels corresponds to a respective color channel of the color filter array. The, device performs the depth estimation by estimating disparity from the color channels of the first and second images.

Abstract: A device comprising an image processor, the image processor being configured to implement: a first machine learning model for performing restoration processing on degraded image data; and a second machine learning model for recognizing areas of an image requiring processing emphasis during the restoration processing; wherein the output of the second machine learning model is an input to the first machine learning model to optimize the restoration processing.

Abstract: An apparatus for denoising an image, the apparatus having a processor configured to receive an input image, implement a trained artificial intelligence model to form an estimate of a noise pattern in the input image and form an output image by subtracting the estimate of the noise pattern from the input image, the model being configured to form the estimate of the noise pattern such that the estimate of the noise pattern is representative of a noise pattern that is characteristic to a specific image sensor type.

Abstract: A device for estimating a scene illumination color for a source image is configured to: determine a set of candidate illuminants and for each of the candidate illuminants, determine a respective correction of the source image; for each of the candidate illuminants, apply the respective correction to the source image to form a corresponding set of corrected images; for each corrected image from the set of corrected images, implement a trained data-driven model to estimate a respective probability of achromaticity of the respective corrected image; and based on the estimated probabilities of achromaticity for the set of corrected images, obtain a final estimate of the scene illumination color for the source image. This approach allows for the evaluation of multiple candidate illuminates to determine an estimate of the scene illumination color.

Abstract: An image processor for transforming an input image, the image processor being configured to implement a trained artificial intelligence model, wherein the image processor is configured to: receive the input image; based on one or both of (i) the content of the input image and (ii) features extracted from the input image, process the image by the trained artificial intelligence model to: (i) determine a set of image filters; and (ii) for each of a plurality of subregions of the image, select an image filter from the set of image filters; and for each of the plurality of subregions of the image, apply the respective image filter to the subregion or to features extracted from that subregion.