公开(公告)号：US11997246B2

公开(公告)日：2024-05-28

申请号：US17721425

申请日：2022-04-15

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Francesca Babiloni ,  Ioannis Marras ,  Ales Leonardis ,  Gregory Slabaugh

IPC: H04N1/64 ,  G06T3/4015

CPC classification number: H04N1/646 ,  G06T3/4015

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.

公开(公告)号：US20230410475A1

公开(公告)日：2023-12-21

申请号：US18460319

申请日：2023-09-01

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Francesca Babiloni ,  Ioannis Marras ,  Jiankang Deng ,  Stefanos Zafeiriou

IPC: G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V10/774 ,  G06V10/56

CPC classification number: G06V10/764 ,  G06V10/776 ,  G06V10/56 ,  G06V10/7747 ,  G06V10/82

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.

公开(公告)号：US12217391B2

公开(公告)日：2025-02-04

申请号：US17742703

申请日：2022-05-12

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Filippos Kokkinos ,  Ioannis Marras ,  Matteo Maggioni ,  Stefanos Zafeiriou ,  Gregory Slabaugh

IPC: G06T5/20 ,  G06V10/44 ,  G06V10/82

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.

公开(公告)号：US11625815B2

公开(公告)日：2023-04-11

申请号：US17030038

申请日：2020-09-23

Applicant: Huawei Technologies Co., Ltd.

Inventor： Gregory Slabaugh ,  Youliang Yan ,  Fenglong Song ,  Gang Chen ,  Jiangwei Li ,  Tao Wang ,  Liu Liu ,  Ioannis Alexiou ,  Ioannis Marras ,  Sean Moran ,  Steven George McDonagh ,  Jose Costa Pereira ,  Viktor Vladimirovich Smirnov

IPC: G06T5/00 ,  G06N3/08 ,  G06T3/40 ,  H04N9/64 ,  H04N9/73 ,  G06N3/084 ,  H04N23/88

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.