公开(公告)号：US20200311871A1

公开(公告)日：2020-10-01

申请号：US16903667

申请日：2020-06-17

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Bing YU ,  Chuanxia ZHENG ,  Bailan FENG

IPC: G06T3/40 ,  G06T5/00 ,  G06T5/50

Abstract: Embodiments of this application provide an image reconstruction method and device. The method includes: inputting a first image into a newly constructed super-resolution model to obtain a reconstructed second image, where a resolution of the second image is higher than that of the first image; the newly constructed super-resolution model is obtained by training an initial super-resolution model by using an error loss; the error loss includes a pixel mean square error and an image feature mean square error; and an image feature includes at least one of a texture feature, a shape feature, a spatial relationship feature, and an image high-level semantic feature. According to the embodiments of this application, quality of a reconstructed image can be improved.

公开(公告)号：US20210012093A1

公开(公告)日：2021-01-14

申请号：US17038208

申请日：2020-09-30

Applicant: Huawei Technologies Co., Ltd. ,  Institute of Automation, Chinese Academy of Sciences

Inventor： Qiang RAO ,  Bing YU ,  Bailan FENG ,  Yibo HU ,  Xiang WU ,  Ran HE ,  Zhenan SUN

IPC: G06K9/00 ,  G06T5/00 ,  G06K9/62 ,  G06T9/00

Abstract: This application provides a method and an apparatus for generating a face rotation image. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, where presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, where the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.

公开(公告)号：US20230222639A1

公开(公告)日：2023-07-13

申请号：US18182655

申请日：2023-03-13

Applicant: Huawei Technologies Co., Ltd.

Inventor： Xinyu ZHANG ,  Bing YU ,  Zhao ZHONG

IPC: G06T5/50 ,  G06T7/00 ,  H04N23/84

CPC classification number: G06T5/50 ,  G06T7/0002 ,  H04N23/84 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/30168

Abstract: This application provides a data processing method, system, and apparatus, and relates to the field of artificial intelligence (AI). The data processing method may be performed by a server, or may be performed by a device having a data processing function. During execution, reference data is first obtained. The reference data includes RGB image data and a device parameter of an image device. Then, a plurality of conversion parameters required for converting the RGB image data into RAW data are determined. Finally, the RGB image data is processed into the RAW data based on the plurality of conversion parameters. The RAW data matches the device parameter of the image device. In this application, the RGB image data is converted into the RAW data based on the plurality of conversion parameters rather than manual experience. Therefore, the described data processing method, system, and apparatus improve data processing efficiency.

公开(公告)号：US20220076000A1

公开(公告)日：2022-03-10

申请号：US17530688

申请日：2021-11-19

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Yun YANG ,  Songjiang LI ,  Bing YU ,  Bailan FENG

IPC: G06K9/00 ,  G06K9/62 ,  G06T3/00 ,  G06T5/50 ,  G06N3/08 ,  G06N3/04

Abstract: An image processing method and apparatus are provided. The image processing method includes: obtaining a face image; separately obtaining a left face image and a right face image based on the face image; inputting the left face image into a first target key point convolutional neural network model, and outputting coordinates of a first left face key point; inputting the right face image into a second target key point convolutional neural network model, and outputting coordinates of a first right face key point; and obtaining coordinates of a face key point of the face image based on the coordinates of the first left face key point and the coordinates of the first right face key point.