{"title":"基于截断总变分法的单雾图像去雾","authors":"Yin Gao, Yijing Su, Jun Li","doi":"10.1145/3421766.3421772","DOIUrl":null,"url":null,"abstract":"Existing dehazing methods are usually to appear visual problems. In the paper, we put forward a truncated total variation method (TTV) to eliminate haze. A histogram analysis is firstly developed to obtain global atmospheric light. Then, using an adaptive boundary constraint TTV to optimize the transmission properly. Finally, a new DCP is presented to remove haze. Shown in experimental results, our method can outperform existent methods on the visual effect.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Single Fog Image Dehazing via Truncated Total Variation Method\",\"authors\":\"Yin Gao, Yijing Su, Jun Li\",\"doi\":\"10.1145/3421766.3421772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Existing dehazing methods are usually to appear visual problems. In the paper, we put forward a truncated total variation method (TTV) to eliminate haze. A histogram analysis is firstly developed to obtain global atmospheric light. Then, using an adaptive boundary constraint TTV to optimize the transmission properly. Finally, a new DCP is presented to remove haze. Shown in experimental results, our method can outperform existent methods on the visual effect.\",\"PeriodicalId\":360184,\"journal\":{\"name\":\"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3421766.3421772\",\"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 2nd International Conference on Artificial Intelligence and Advanced Manufacture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3421766.3421772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Single Fog Image Dehazing via Truncated Total Variation Method
Existing dehazing methods are usually to appear visual problems. In the paper, we put forward a truncated total variation method (TTV) to eliminate haze. A histogram analysis is firstly developed to obtain global atmospheric light. Then, using an adaptive boundary constraint TTV to optimize the transmission properly. Finally, a new DCP is presented to remove haze. Shown in experimental results, our method can outperform existent methods on the visual effect.
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