Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement
Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/759 |
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| author | Fei Li Li Wan Jiangbin Zheng Lu Wang Yue Xi |
| author_facet | Fei Li Li Wan Jiangbin Zheng Lu Wang Yue Xi |
| author_sort | Fei Li |
| collection | DOAJ |
| description | Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively. |
| format | Article |
| id | doaj-art-8b18d2fc4d0841549773b62d9bf503d0 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-8b18d2fc4d0841549773b62d9bf503d02025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-02-0117575910.3390/rs17050759Contrastive Feature Disentanglement via Physical Priors for Underwater Image EnhancementFei Li0Li Wan1Jiangbin Zheng2Lu Wang3Yue Xi4School of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaHangzhou Institute of Technology, Xidian University, Hangzhou 311200, ChinaSchool of Software and Microelectronics, Northwestern Polytechnical University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaGuangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaUnderwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively.https://www.mdpi.com/2072-4292/17/5/759contrastive feature disentanglementphysical priorsunderwater image enhancement |
| spellingShingle | Fei Li Li Wan Jiangbin Zheng Lu Wang Yue Xi Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement Remote Sensing contrastive feature disentanglement physical priors underwater image enhancement |
| title | Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement |
| title_full | Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement |
| title_fullStr | Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement |
| title_full_unstemmed | Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement |
| title_short | Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement |
| title_sort | contrastive feature disentanglement via physical priors for underwater image enhancement |
| topic | contrastive feature disentanglement physical priors underwater image enhancement |
| url | https://www.mdpi.com/2072-4292/17/5/759 |
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