Underwater image enhancement via multiscale disentanglement strategy
Abstract Underwater images suffer from color casts, low illumination, and blurred details caused by light absorption and scattering in water. Existing data-driven methods often overlook the scene characteristics of underwater imaging, limiting their expressive power. To address the above issues, we...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89109-7 |
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| author | Jiaquan Yan Hao Hu Yijian Wang Muhammad Wasim Nawaz Naveed Ur Rehman Junejo Ente Guo Huibin Feng |
| author_facet | Jiaquan Yan Hao Hu Yijian Wang Muhammad Wasim Nawaz Naveed Ur Rehman Junejo Ente Guo Huibin Feng |
| author_sort | Jiaquan Yan |
| collection | DOAJ |
| description | Abstract Underwater images suffer from color casts, low illumination, and blurred details caused by light absorption and scattering in water. Existing data-driven methods often overlook the scene characteristics of underwater imaging, limiting their expressive power. To address the above issues, we propose a Multiscale Disentanglement Network (MD-Net) for Underwater Image Enhancement (UIE), which mainly consists of scene radiance disentanglement (SRD) and transmission map disentanglement (TMD) modules. Specifically, MD-Net first disentangles original images into three physical parameters which are scene radiance (clear image), transmission map, and global background light. The proposed network then reconstructs these physical parameters into underwater images. Furthermore, MD-Net introduces class adversarial learning between the original and reconstructed images to supervise the disentanglement accuracy of the network. Moreover, we design a multi-level fusion module (MFM) and dual-layer weight estimation unit (DWEU) for color cast adjustment and visibility enhancement. Finally, we conduct extensive qualitative and quantitative experiments on three benchmark datasets, which demonstrate that our approach outperforms other traditional and state-of-the-art methods. Our code and results are available at: https://github.com/WYJGR/MD-Net . |
| format | Article |
| id | doaj-art-047c174e5486403f8a6a9eeb132a85c8 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-047c174e5486403f8a6a9eeb132a85c82025-08-20T02:15:11ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-89109-7Underwater image enhancement via multiscale disentanglement strategyJiaquan Yan0Hao Hu1Yijian Wang2Muhammad Wasim Nawaz3Naveed Ur Rehman Junejo4Ente Guo5Huibin Feng6The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang UniversityThe Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang UniversitySchool of Ophthalmology and Optometry, Wenzhou Medical UniversityDepartment of Computer Engineering, The University of LahoreDepartment of Computer Engineering, The University of LahoreThe Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang UniversityThe Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, School of Computer and Big Data, Minjiang UniversityAbstract Underwater images suffer from color casts, low illumination, and blurred details caused by light absorption and scattering in water. Existing data-driven methods often overlook the scene characteristics of underwater imaging, limiting their expressive power. To address the above issues, we propose a Multiscale Disentanglement Network (MD-Net) for Underwater Image Enhancement (UIE), which mainly consists of scene radiance disentanglement (SRD) and transmission map disentanglement (TMD) modules. Specifically, MD-Net first disentangles original images into three physical parameters which are scene radiance (clear image), transmission map, and global background light. The proposed network then reconstructs these physical parameters into underwater images. Furthermore, MD-Net introduces class adversarial learning between the original and reconstructed images to supervise the disentanglement accuracy of the network. Moreover, we design a multi-level fusion module (MFM) and dual-layer weight estimation unit (DWEU) for color cast adjustment and visibility enhancement. Finally, we conduct extensive qualitative and quantitative experiments on three benchmark datasets, which demonstrate that our approach outperforms other traditional and state-of-the-art methods. Our code and results are available at: https://github.com/WYJGR/MD-Net .https://doi.org/10.1038/s41598-025-89109-7Underwater image enhancementDisentanglement strategyMultiscale feature fusionUnderwater optical imaging |
| spellingShingle | Jiaquan Yan Hao Hu Yijian Wang Muhammad Wasim Nawaz Naveed Ur Rehman Junejo Ente Guo Huibin Feng Underwater image enhancement via multiscale disentanglement strategy Scientific Reports Underwater image enhancement Disentanglement strategy Multiscale feature fusion Underwater optical imaging |
| title | Underwater image enhancement via multiscale disentanglement strategy |
| title_full | Underwater image enhancement via multiscale disentanglement strategy |
| title_fullStr | Underwater image enhancement via multiscale disentanglement strategy |
| title_full_unstemmed | Underwater image enhancement via multiscale disentanglement strategy |
| title_short | Underwater image enhancement via multiscale disentanglement strategy |
| title_sort | underwater image enhancement via multiscale disentanglement strategy |
| topic | Underwater image enhancement Disentanglement strategy Multiscale feature fusion Underwater optical imaging |
| url | https://doi.org/10.1038/s41598-025-89109-7 |
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