Underwater Image Enhancement Based on Generative Adversarial Networks
This paper proposes an underwater image correction and enhancement algorithm based on generative adversarial networks. In this algorithm, the multi-scale kernel is applied to the improved residual module to construct a generator, which realizes the extraction and fusion of multiple receptive fields...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2022-02-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-2-134.shtml |
| _version_ | 1818918651468709888 |
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| author | LI Yu, YANG Daoyong, LIU Lingya, WANG Yiyin |
| author_facet | LI Yu, YANG Daoyong, LIU Lingya, WANG Yiyin |
| author_sort | LI Yu, YANG Daoyong, LIU Lingya, WANG Yiyin |
| collection | DOAJ |
| description | This paper proposes an underwater image correction and enhancement algorithm based on generative adversarial networks. In this algorithm, the multi-scale kernel is applied to the improved residual module to construct a generator, which realizes the extraction and fusion of multiple receptive fields feature information. The discriminator design considers the relationship between global information and local details, and establishes a global-region dual discriminator structure, which can ensure the consistency of overall style and edge texture. An unsupervised loss function based on human visual sensory system is proposed. Reference image constraints are not required, and the confrontation loss and the content loss are jointly optimized to obtain better color and structure performance. Experimental evaluations on multiple data sets show that the proposed algorithm can better correct color deviation and contrast, protect details from loss, and is superior to typical algorithms in subjective and objective indexes. |
| first_indexed | 2024-12-20T00:53:21Z |
| format | Article |
| id | doaj.art-533b475b5efe4d17831c7e44e2ce09e8 |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-12-20T00:53:21Z |
| publishDate | 2022-02-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-533b475b5efe4d17831c7e44e2ce09e82022-12-21T19:59:11ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672022-02-0156213414210.16183/j.cnki.jsjtu.2021.075Underwater Image Enhancement Based on Generative Adversarial NetworksLI Yu, YANG Daoyong, LIU Lingya, WANG Yiyin0School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaThis paper proposes an underwater image correction and enhancement algorithm based on generative adversarial networks. In this algorithm, the multi-scale kernel is applied to the improved residual module to construct a generator, which realizes the extraction and fusion of multiple receptive fields feature information. The discriminator design considers the relationship between global information and local details, and establishes a global-region dual discriminator structure, which can ensure the consistency of overall style and edge texture. An unsupervised loss function based on human visual sensory system is proposed. Reference image constraints are not required, and the confrontation loss and the content loss are jointly optimized to obtain better color and structure performance. Experimental evaluations on multiple data sets show that the proposed algorithm can better correct color deviation and contrast, protect details from loss, and is superior to typical algorithms in subjective and objective indexes.http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-2-134.shtmlunderwater image enhancementgenerative adversarial networksresidual structureunsupervised learning |
| spellingShingle | LI Yu, YANG Daoyong, LIU Lingya, WANG Yiyin Underwater Image Enhancement Based on Generative Adversarial Networks Shanghai Jiaotong Daxue xuebao underwater image enhancement generative adversarial networks residual structure unsupervised learning |
| title | Underwater Image Enhancement Based on Generative Adversarial Networks |
| title_full | Underwater Image Enhancement Based on Generative Adversarial Networks |
| title_fullStr | Underwater Image Enhancement Based on Generative Adversarial Networks |
| title_full_unstemmed | Underwater Image Enhancement Based on Generative Adversarial Networks |
| title_short | Underwater Image Enhancement Based on Generative Adversarial Networks |
| title_sort | underwater image enhancement based on generative adversarial networks |
| topic | underwater image enhancement generative adversarial networks residual structure unsupervised learning |
| url | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-2-134.shtml |
| work_keys_str_mv | AT liyuyangdaoyongliulingyawangyiyin underwaterimageenhancementbasedongenerativeadversarialnetworks |