Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study
Underwater remote sensing image enhancement is complicated by low illumination, color bias, and blurriness, affecting deep-sea monitoring and marine resource development. This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing....
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/5/899 |
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| author | Yunsheng Ma Yanan Cheng Dapeng Zhang |
| author_facet | Yunsheng Ma Yanan Cheng Dapeng Zhang |
| author_sort | Yunsheng Ma |
| collection | DOAJ |
| description | Underwater remote sensing image enhancement is complicated by low illumination, color bias, and blurriness, affecting deep-sea monitoring and marine resource development. This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing. The physical model, based on the Jaffe–McGlamery model, integrates multi-scale histogram equalization, wavelength compensation, and Laplacian sharpening, using cluster analysis to target enhancements. It performs well in shallow, stable waters (turbidity < 20 NTU, depth < 10 m, PSNR = 12.2) but struggles in complex environments (turbidity > 30 NTU). Deep learning models, including water-net, UWCNN, UWCycleGAN, and U-shape Transformer, excel in dynamic conditions, achieving UIQM = 0.24, though requiring GPU support for real-time use. Evaluated on the UIEB dataset (890 images), the physical model suits specific scenarios, while deep learning adapts better to variable underwater settings. These findings offer a theoretical and technical basis for underwater image enhancement and support sustainable marine resource use. |
| format | Article |
| id | doaj-art-bee26b33bb32498a8ec73810c91b4204 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-bee26b33bb32498a8ec73810c91b42042025-08-20T03:48:01ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113589910.3390/jmse13050899Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative StudyYunsheng Ma0Yanan Cheng1Dapeng Zhang2Ship and Maritime College, Guangdong Ocean University, Zhanjiang 524005, ChinaTaizhou Institute of Science & Technology, College of Business, NJUST, Taizhou 225300, ChinaShip and Maritime College, Guangdong Ocean University, Zhanjiang 524005, ChinaUnderwater remote sensing image enhancement is complicated by low illumination, color bias, and blurriness, affecting deep-sea monitoring and marine resource development. This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing. The physical model, based on the Jaffe–McGlamery model, integrates multi-scale histogram equalization, wavelength compensation, and Laplacian sharpening, using cluster analysis to target enhancements. It performs well in shallow, stable waters (turbidity < 20 NTU, depth < 10 m, PSNR = 12.2) but struggles in complex environments (turbidity > 30 NTU). Deep learning models, including water-net, UWCNN, UWCycleGAN, and U-shape Transformer, excel in dynamic conditions, achieving UIQM = 0.24, though requiring GPU support for real-time use. Evaluated on the UIEB dataset (890 images), the physical model suits specific scenarios, while deep learning adapts better to variable underwater settings. These findings offer a theoretical and technical basis for underwater image enhancement and support sustainable marine resource use.https://www.mdpi.com/2077-1312/13/5/899underwater remote sensingdeep learning algorithmsmulti-scale fusion-enhanced physical modelunderwater image enhancement |
| spellingShingle | Yunsheng Ma Yanan Cheng Dapeng Zhang Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study Journal of Marine Science and Engineering underwater remote sensing deep learning algorithms multi-scale fusion-enhanced physical model underwater image enhancement |
| title | Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study |
| title_full | Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study |
| title_fullStr | Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study |
| title_full_unstemmed | Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study |
| title_short | Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study |
| title_sort | comparative analysis of traditional and deep learning approaches for underwater remote sensing image enhancement a quantitative study |
| topic | underwater remote sensing deep learning algorithms multi-scale fusion-enhanced physical model underwater image enhancement |
| url | https://www.mdpi.com/2077-1312/13/5/899 |
| work_keys_str_mv | AT yunshengma comparativeanalysisoftraditionalanddeeplearningapproachesforunderwaterremotesensingimageenhancementaquantitativestudy AT yanancheng comparativeanalysisoftraditionalanddeeplearningapproachesforunderwaterremotesensingimageenhancementaquantitativestudy AT dapengzhang comparativeanalysisoftraditionalanddeeplearningapproachesforunderwaterremotesensingimageenhancementaquantitativestudy |