The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 22, 2024

Filed:

Mar. 29, 2022
Applicant:

Tencent Technology (Shenzhen) Company Limited, Guangdong, CN;

Inventors:

Xinpeng Xie, Guangdong, CN;

Jiawei Chen, Guangdong, CN;

Yuexiang Li, Guangdong, CN;

Kai Ma, Guangdong, CN;

Yefeng Zheng, Guangdong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); A61K 35/12 (2015.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 19/00 (2011.01);
U.S. Cl.
CPC ...
G06T 5/50 (2013.01); G06T 7/97 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

An image processing method includes obtaining a sample image and a generative adversarial network (GAN), including a generation network and an adversarial network, and performing style conversion on the sample image, to obtain a reference image. The method further includes performing global style recognition on the reference image, to determine a global style loss between the reference image and the sample image, and performing image content recognition on the reference image and the sample image, to determine a content loss between the reference image and the sample image. The method also includes performing local style recognition on the reference image and the sample image, to determine a local style loss of the reference image and a local style loss of the sample image, training the generation network to obtain a trained generation network, and performing style conversion on a to-be-processed image by using the trained generation network.


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