Research on Image Style Transfer Method Based on Semantic Adaptive.

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Bibliographic Details
Title:	Research on Image Style Transfer Method Based on Semantic Adaptive.
Authors:	MA Chi¹ machi@hzu.edu.cn, Wang Shaofan² wsf19961230@163.com, Hu Hui³ Huhui@hzu.edu.cn
Source:	IAENG International Journal of Computer Science. Nov2025, Vol. 52 Issue 11, p4140-4149. 10p.
Subjects:	Image representation, Generative adversarial networks, Image processing software, Comparative studies, Image processing
Abstract:	This paper proposes a novel image style transfer technique based on semantic adaptation to address the problems of content image representation and semantic information loss during the image style transfer process. Specifically, Two modules make up the method: the representation transfer module and the semantic transfer module. The representation transfer module extracts the content image's representation features through context coding. The semantic transfer module extracts the content image's semantic features by generating an adversarial network. These modules effectively preserve the content image's information at both the representation and semantic levels. In comparative experiments with various image style transfer methods, our proposed method achieves significantly better results. Thus, the proposed method effectively retains the representation and semantic information of content images during the style transfer process. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper proposes a novel image style transfer technique based on semantic adaptation to address the problems of content image representation and semantic information loss during the image style transfer process. Specifically, Two modules make up the method: the representation transfer module and the semantic transfer module. The representation transfer module extracts the content image's representation features through context coding. The semantic transfer module extracts the content image's semantic features by generating an adversarial network. These modules effectively preserve the content image's information at both the representation and semantic levels. In comparative experiments with various image style transfer methods, our proposed method achieves significantly better results. Thus, the proposed method effectively retains the representation and semantic information of content images during the style transfer process. [ABSTRACT FROM AUTHOR]
ISSN:	1819656X