{"title":"基于亮度参考模型的单幅图像去雾","authors":"Jiafeng Li, Hong Zhang, Ding Yuan, Helong Wang","doi":"10.1109/ACPR.2013.119","DOIUrl":null,"url":null,"abstract":"Optical transmission estimation is a key procedure for removing haze from certain outdoor images. In this paper, we propose a novel transmission estimation model called the luminance reference model. A luminance reference, which is the intensity lower bound of a local region in the haze free image, is assumed to be a global constant across the image. Based on this assumption, we theoretically prove that, with an appropriate luminance reference, the transmission can be estimated accurately. By using a scene-dependent estimate of the luminance reference, our method can be applied to different types of images. We further propose a two-step guided approach to rapid and robust computation of a transmission map. Our experimental results show that the proposed method is computationally efficient, while producing comparable visual results to the existing state-of-the-art, but more complex methods.","PeriodicalId":365633,"journal":{"name":"2013 2nd IAPR Asian Conference on Pattern Recognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Haze Removal from Single Images Based on a Luminance Reference Model\",\"authors\":\"Jiafeng Li, Hong Zhang, Ding Yuan, Helong Wang\",\"doi\":\"10.1109/ACPR.2013.119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Optical transmission estimation is a key procedure for removing haze from certain outdoor images. In this paper, we propose a novel transmission estimation model called the luminance reference model. A luminance reference, which is the intensity lower bound of a local region in the haze free image, is assumed to be a global constant across the image. Based on this assumption, we theoretically prove that, with an appropriate luminance reference, the transmission can be estimated accurately. By using a scene-dependent estimate of the luminance reference, our method can be applied to different types of images. We further propose a two-step guided approach to rapid and robust computation of a transmission map. Our experimental results show that the proposed method is computationally efficient, while producing comparable visual results to the existing state-of-the-art, but more complex methods.\",\"PeriodicalId\":365633,\"journal\":{\"name\":\"2013 2nd IAPR Asian Conference on Pattern Recognition\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 2nd IAPR Asian Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACPR.2013.119\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 2nd IAPR Asian Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2013.119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Haze Removal from Single Images Based on a Luminance Reference Model
Optical transmission estimation is a key procedure for removing haze from certain outdoor images. In this paper, we propose a novel transmission estimation model called the luminance reference model. A luminance reference, which is the intensity lower bound of a local region in the haze free image, is assumed to be a global constant across the image. Based on this assumption, we theoretically prove that, with an appropriate luminance reference, the transmission can be estimated accurately. By using a scene-dependent estimate of the luminance reference, our method can be applied to different types of images. We further propose a two-step guided approach to rapid and robust computation of a transmission map. Our experimental results show that the proposed method is computationally efficient, while producing comparable visual results to the existing state-of-the-art, but more complex methods.