Abstract: The present disclosure relates to method and apparatuses for denoising an image. One example method includes receiving an input image captured by an image sensor, implement a trained artificial intelligence model to form an estimate of a noise pattern in the input image, to form an estimate of at least one noise statistic for the image sensor, and to refine the estimate of the noise pattern based on the estimate of the at least one noise statistic, and form an output image by subtracting the refined estimate of the noise pattern from the input image.

Abstract: A classification apparatus for allocating a raw image to a respective class out of a plurality of classes, the apparatus comprising one or more processors and a memory storing in non-transient form data defining program code executable by the one or more processors to implement an image classification model, the apparatus being configured to: receive a raw image; generate a plurality of raw matrices from the raw image, wherein each of the plurality of raw matrices is formed by one spatial value and one characteristic value; and allocate the raw image to the respective class depending on the plurality of raw matrices. By forming each of the raw matrices by one spatial value and one characteristic value the computational cost may be reduced.

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: 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: 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.

Abstract: An image processing device for identifying one or more characteristics of an input image, the device including a processor configured to: receive the input image, the input image extending along a first axis and a second axis; form a series of attribute maps based on the received input image; perform a first correlation operation by identifying regions in respect of which the patterns of multiple ones of the series of attribute maps are correlated, and forming a first output in dependence on that operation; perform a second correlation operation for identifying combinations of (i) attributes and (ii) portions of the image having common location in terms of the first axis, and forming a second output in dependence on that operation; and form a representation of the one or more characteristics of the input image in dependence on at least the first output and the second output.

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

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.