Huawei Technologies Co., Limited

Ioannis Marras

Gregory Gibran Slabaugh

Stefanos Zafeiriou

Gregory Slabaugh

Ioannis Alexiou

Filippos Kokkinos

Tao Wang

Liu Liu

Gang Chen

Jiangwei Li

Sean Moran

Youliang Yan

Fenglong Song

Ales Leonardis

Steven George McDonagh

Francesca Babiloni

Matteo Maggioni

Jose Costa Pereira

Viktor Vladimirovich Smirnov

Stamatios Lefkimmiatis

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.

Noise reconstruction for image denoising

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.

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.

Spatially adaptive image filtering

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.

Trained artificial intelligence model for raw to RGB image transformation

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.

Image processor and method

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London, United Kingdom

Ioannis Marras