Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images
The increasing application of underwater robotic systems in deep-sea exploration, inspection, and resource extraction has created a strong demand for reliable visual perception under challenging conditions. However, image quality is severely degraded in low-light underwater environments due to the c...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1092 |
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| author | Dechuan Kong Jinglong Mao Yandi Zhang Xiaohu Zhao Yanyan Wang Shungang Wang |
| author_facet | Dechuan Kong Jinglong Mao Yandi Zhang Xiaohu Zhao Yanyan Wang Shungang Wang |
| author_sort | Dechuan Kong |
| collection | DOAJ |
| description | The increasing application of underwater robotic systems in deep-sea exploration, inspection, and resource extraction has created a strong demand for reliable visual perception under challenging conditions. However, image quality is severely degraded in low-light underwater environments due to the combined effects of light absorption and scattering, resulting in color imbalance, low contrast, and illumination instability. These factors limit the effectiveness of visual-based autonomous operations. We propose ATS-UGAN, a Dual-domain Adaptive Synergy Generative Adversarial Network for low-light underwater image enhancement to confront the above issues. The network integrates Multi-scale Hybrid Attention (MHA) that synergizes spatial and frequency domain representations to capture key image features adaptively. An Adaptive Parameterized Convolution (AP-Conv) module is introduced to handle non-uniform scattering by dynamically adjusting convolution kernels through a multi-branch design. In addition, a Dynamic Content-aware Markovian Discriminator (DCMD) is employed to perceive the dual-domain information synergistically, enhancing image texture realism and improving color correction. Extensive experiments on benchmark underwater datasets demonstrate that ATS-UGAN surpasses state-of-the-art approaches, achieving 28.7/0.92 PSNR/SSIM on EUVP and 28.2/0.91 on UFO-120. Additional reference and no-reference metrics further confirm the improved visual quality and realism of the enhanced images. |
| format | Article |
| id | doaj-art-ece026c43c3346259f01fb7dbf941648 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ece026c43c3346259f01fb7dbf9416482025-08-20T02:21:07ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136109210.3390/jmse13061092Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater ImagesDechuan Kong0Jinglong Mao1Yandi Zhang2Xiaohu Zhao3Yanyan Wang4Shungang Wang5National and Local Joint Engineering Laboratory of Internet Application Technology on Mine, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, ChinaNational and Local Joint Engineering Laboratory of Internet Application Technology on Mine, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang 453003, ChinaSchool of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang 453003, ChinaThe increasing application of underwater robotic systems in deep-sea exploration, inspection, and resource extraction has created a strong demand for reliable visual perception under challenging conditions. However, image quality is severely degraded in low-light underwater environments due to the combined effects of light absorption and scattering, resulting in color imbalance, low contrast, and illumination instability. These factors limit the effectiveness of visual-based autonomous operations. We propose ATS-UGAN, a Dual-domain Adaptive Synergy Generative Adversarial Network for low-light underwater image enhancement to confront the above issues. The network integrates Multi-scale Hybrid Attention (MHA) that synergizes spatial and frequency domain representations to capture key image features adaptively. An Adaptive Parameterized Convolution (AP-Conv) module is introduced to handle non-uniform scattering by dynamically adjusting convolution kernels through a multi-branch design. In addition, a Dynamic Content-aware Markovian Discriminator (DCMD) is employed to perceive the dual-domain information synergistically, enhancing image texture realism and improving color correction. Extensive experiments on benchmark underwater datasets demonstrate that ATS-UGAN surpasses state-of-the-art approaches, achieving 28.7/0.92 PSNR/SSIM on EUVP and 28.2/0.91 on UFO-120. Additional reference and no-reference metrics further confirm the improved visual quality and realism of the enhanced images.https://www.mdpi.com/2077-1312/13/6/1092dual-domain synergyadaptive enhancementlow-light underwater image enhancementgenerative adversarial network |
| spellingShingle | Dechuan Kong Jinglong Mao Yandi Zhang Xiaohu Zhao Yanyan Wang Shungang Wang Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images Journal of Marine Science and Engineering dual-domain synergy adaptive enhancement low-light underwater image enhancement generative adversarial network |
| title | Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images |
| title_full | Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images |
| title_fullStr | Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images |
| title_full_unstemmed | Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images |
| title_short | Dual-Domain Adaptive Synergy GAN for Enhancing Low-Light Underwater Images |
| title_sort | dual domain adaptive synergy gan for enhancing low light underwater images |
| topic | dual-domain synergy adaptive enhancement low-light underwater image enhancement generative adversarial network |
| url | https://www.mdpi.com/2077-1312/13/6/1092 |
| work_keys_str_mv | AT dechuankong dualdomainadaptivesynergyganforenhancinglowlightunderwaterimages AT jinglongmao dualdomainadaptivesynergyganforenhancinglowlightunderwaterimages AT yandizhang dualdomainadaptivesynergyganforenhancinglowlightunderwaterimages AT xiaohuzhao dualdomainadaptivesynergyganforenhancinglowlightunderwaterimages AT yanyanwang dualdomainadaptivesynergyganforenhancinglowlightunderwaterimages AT shungangwang dualdomainadaptivesynergyganforenhancinglowlightunderwaterimages |