公开(公告)号：US12124962B2

公开(公告)日：2024-10-22

申请号：US17726684

申请日：2022-04-22

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Axel Barroso Laguna ,  Yannick Verdie ,  Benjamin Busam ,  Ales Leonardis ,  Gregory Slabaugh

IPC: G06N3/088 ,  G06V10/25 ,  G06V10/44 ,  G06V10/46 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06N3/088 ,  G06V10/25 ,  G06V10/454 ,  G06V10/462 ,  G06V10/774 ,  G06V10/82

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.

公开(公告)号：US12260575B2

公开(公告)日：2025-03-25

申请号：US17513596

申请日：2021-10-28

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Benjamin Busam ,  Gregory Slabaugh

IPC: G06K9/00 ,  G06T3/18 ,  G06T3/40 ,  G06T7/55 ,  G06T7/60 ,  G06T7/73

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.

公开(公告)号：US12190488B2

公开(公告)日：2025-01-07

申请号：US17480308

申请日：2021-09-21

Applicant: Huawei Technologies Co., Ltd.

Inventor： Sean Moran ,  Gregory Slabaugh

IPC: G06T5/92 ,  G06N20/00 ,  G06T5/10 ,  G06T5/20 ,  G06T5/70

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.

公开(公告)号：US12143730B2

公开(公告)日：2024-11-12

申请号：US17031423

申请日：2020-09-24

Applicant: Huawei Technologies Co., Ltd.

Inventor： Steven George McDonagh ,  Sarah Parisot ,  Gregory Slabaugh ,  Zhenguo Li

IPC: H04N23/88 ,  G06T7/90 ,  H04N1/60 ,  G06N3/0985

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.

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：US11943419B2

公开(公告)日：2024-03-26

申请号：US17479911

申请日：2021-09-20

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Benjamin Busam ,  Matthieu Hog ,  Steven George McDonagh ,  Gregory Slabaugh

IPC: G06T7/593 ,  G06N20/00 ,  H04N13/128 ,  H04N13/00

CPC classification number: H04N13/128 ,  G06N20/00 ,  G06T7/593 ,  G06T2207/10028 ,  H04N2013/0081

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.

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：US12125223B2

公开(公告)日：2024-10-22

申请号：US17586034

申请日：2022-01-27

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Benjamin Busam ,  Patrick Ruhkamp ,  Matthieu Hog ,  Yannick Verdie ,  Ales Leonardis ,  Gregory Slabaugh

IPC: G06T7/514

CPC classification number: G06T7/514 ,  G06T2207/10024 ,  G06T2207/20081

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.

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.

公开(公告)号：US20250103891A1

公开(公告)日：2025-03-27

申请号：US18906358

申请日：2024-10-04

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Axel BARROSO LAGUNA ,  Yannick Verdie ,  Benjamin Busam ,  Ales Leonardis ,  Gregory Slabaugh

IPC: G06N3/088 ,  G06V10/25 ,  G06V10/44 ,  G06V10/46 ,  G06V10/774 ,  G06V10/82

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.