DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network
The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between c...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/7/561 |
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| author | Na Su Jingtao Wang Jingjing Zhang Ying Li Yun Pan |
| author_facet | Na Su Jingtao Wang Jingjing Zhang Ying Li Yun Pan |
| author_sort | Na Su |
| collection | DOAJ |
| description | The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches. |
| format | Article |
| id | doaj-art-41a9948f9f07421faae9035dcc380e68 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-41a9948f9f07421faae9035dcc380e682025-08-20T03:08:06ZengMDPI AGInformation2078-24892025-06-0116756110.3390/info16070561DBDST-Net: Dual-Branch Decoupled Image Style Transfer NetworkNa Su0Jingtao Wang1Jingjing Zhang2Ying Li3Yun Pan4State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, ChinaThe image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches.https://www.mdpi.com/2078-2489/16/7/561image style transferdiffusion modulefeature decouplingdegradation representations |
| spellingShingle | Na Su Jingtao Wang Jingjing Zhang Ying Li Yun Pan DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network Information image style transfer diffusion module feature decoupling degradation representations |
| title | DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network |
| title_full | DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network |
| title_fullStr | DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network |
| title_full_unstemmed | DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network |
| title_short | DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network |
| title_sort | dbdst net dual branch decoupled image style transfer network |
| topic | image style transfer diffusion module feature decoupling degradation representations |
| url | https://www.mdpi.com/2078-2489/16/7/561 |
| work_keys_str_mv | AT nasu dbdstnetdualbranchdecoupledimagestyletransfernetwork AT jingtaowang dbdstnetdualbranchdecoupledimagestyletransfernetwork AT jingjingzhang dbdstnetdualbranchdecoupledimagestyletransfernetwork AT yingli dbdstnetdualbranchdecoupledimagestyletransfernetwork AT yunpan dbdstnetdualbranchdecoupledimagestyletransfernetwork |