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Research on Image Style Transfer Method Based on Semantic Adaptive.

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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]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Research on Image Style Transfer Method Based on Semantic Adaptive.
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  Data: <searchLink fieldCode="AR" term="%22MA+Chi%22">MA Chi</searchLink><relatesTo>1</relatesTo><i> machi@hzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang+Shaofan%22">Wang Shaofan</searchLink><relatesTo>2</relatesTo><i> wsf19961230@163.com</i><br /><searchLink fieldCode="AR" term="%22Hu+Hui%22">Hu Hui</searchLink><relatesTo>3</relatesTo><i> Huhui@hzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Nov2025, Vol. 52 Issue 11, p4140-4149. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Image+representation%22">Image representation</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing+software%22">Image processing software</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Code: eng
        Text: English
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      – SubjectFull: Image representation
        Type: general
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Image processing software
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Image processing
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      – TitleFull: Research on Image Style Transfer Method Based on Semantic Adaptive.
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            NameFull: MA Chi
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            NameFull: Wang Shaofan
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              M: 11
              Text: Nov2025
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              Y: 2025
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            – TitleFull: IAENG International Journal of Computer Science
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