{"title":"Single Image Dehazing Using Non-local Total Generalized Variation","authors":"Renjie He, Xiucai Huang","doi":"10.1109/ICIEA.2019.8833710","DOIUrl":null,"url":null,"abstract":"Single image dehazing has been a challenging problem due to its ill-posed nature. In this paper, a novel single image dehazing approach is proposed to accurately model the transmission map and suppress artifacts in the recovered haze-free image. Firstly, a coarse transmission is estimated using the patch based haze-line model. After that, a non-local Total Generalized Variation regularization is introduced to refine the transmission while preserving the local smoothness property and depth discontinuities. In addition, a regularized optimization is proposed for recovering the scene radiance without bringing artifacts boosting. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results indicate that the proposed method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.","PeriodicalId":311302,"journal":{"name":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"449 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2019.8833710","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Single image dehazing has been a challenging problem due to its ill-posed nature. In this paper, a novel single image dehazing approach is proposed to accurately model the transmission map and suppress artifacts in the recovered haze-free image. Firstly, a coarse transmission is estimated using the patch based haze-line model. After that, a non-local Total Generalized Variation regularization is introduced to refine the transmission while preserving the local smoothness property and depth discontinuities. In addition, a regularized optimization is proposed for recovering the scene radiance without bringing artifacts boosting. Compared with the state-of-the-art dehazing methods, both quantitative and qualitative experimental results indicate that the proposed method is capable of obtaining an accurate transmission map and a visually plausible dehazed image.