{"title":"基于双对抗网络的水下图像增强","authors":"Zhengao Wang, Tanlin Li, Wenzheng Qu","doi":"10.1145/3406971.3406976","DOIUrl":null,"url":null,"abstract":"Since the degradation of the image has seriously constrained the development of marine research, the underwater image enhancement has been paid more and more attentions. Due to the diversity of underwater images (for example, underwater images show different attenuation and color bias in different scenes) and the lack of underwater datasets, most existing methods usually show satisfactory results on some kinds of underwater types. To solve the problems, we built a novel model, including two adversarial network blocks, to learn the essential content features of multiple underwater types and restore high quality images. We trained the model under the synthetic dataset based on Jerlov underwater type image dataset. Experimental results show that the model not only outperforms most previous methods in PSNR and UIQM but also shows the generalization ability.","PeriodicalId":111905,"journal":{"name":"Proceedings of the 4th International Conference on Graphics and Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Underwater Image Enhancement Using Dual Adversarial Network\",\"authors\":\"Zhengao Wang, Tanlin Li, Wenzheng Qu\",\"doi\":\"10.1145/3406971.3406976\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the degradation of the image has seriously constrained the development of marine research, the underwater image enhancement has been paid more and more attentions. Due to the diversity of underwater images (for example, underwater images show different attenuation and color bias in different scenes) and the lack of underwater datasets, most existing methods usually show satisfactory results on some kinds of underwater types. To solve the problems, we built a novel model, including two adversarial network blocks, to learn the essential content features of multiple underwater types and restore high quality images. We trained the model under the synthetic dataset based on Jerlov underwater type image dataset. Experimental results show that the model not only outperforms most previous methods in PSNR and UIQM but also shows the generalization ability.\",\"PeriodicalId\":111905,\"journal\":{\"name\":\"Proceedings of the 4th International Conference on Graphics and Signal Processing\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Conference on Graphics and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3406971.3406976\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th International Conference on Graphics and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3406971.3406976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Underwater Image Enhancement Using Dual Adversarial Network
Since the degradation of the image has seriously constrained the development of marine research, the underwater image enhancement has been paid more and more attentions. Due to the diversity of underwater images (for example, underwater images show different attenuation and color bias in different scenes) and the lack of underwater datasets, most existing methods usually show satisfactory results on some kinds of underwater types. To solve the problems, we built a novel model, including two adversarial network blocks, to learn the essential content features of multiple underwater types and restore high quality images. We trained the model under the synthetic dataset based on Jerlov underwater type image dataset. Experimental results show that the model not only outperforms most previous methods in PSNR and UIQM but also shows the generalization ability.