{"title":"一种多尺度视网膜图像增强算法","authors":"Yuehu Liu, Yuanqi Su, Yunfeng Zhu, Zejian Yuan","doi":"10.1109/ICVES.2005.1563628","DOIUrl":null,"url":null,"abstract":"Rapidly and robust image enhancement transformation is important for numerous applications in intelligent video surveillance, which can improve the quality of scene image and extract better features in complex environment where the images undergo large lighting changes. In this paper, a multi-scale retinex algorithm is presented for image enhancement, which has two contributions. First, the initial approximation image is computed by both each pixel value and maximum value of original image. Second, discrete wavelet transformation is used to decrease computation complexity. Experimental tests on numerous scene images show that proposed algorithm has better performance for edge detecting from surveillance images and lane images.","PeriodicalId":443433,"journal":{"name":"IEEE International Conference on Vehicular Electronics and Safety, 2005.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A multi-scale retinex algorithm for image enhancement\",\"authors\":\"Yuehu Liu, Yuanqi Su, Yunfeng Zhu, Zejian Yuan\",\"doi\":\"10.1109/ICVES.2005.1563628\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rapidly and robust image enhancement transformation is important for numerous applications in intelligent video surveillance, which can improve the quality of scene image and extract better features in complex environment where the images undergo large lighting changes. In this paper, a multi-scale retinex algorithm is presented for image enhancement, which has two contributions. First, the initial approximation image is computed by both each pixel value and maximum value of original image. Second, discrete wavelet transformation is used to decrease computation complexity. Experimental tests on numerous scene images show that proposed algorithm has better performance for edge detecting from surveillance images and lane images.\",\"PeriodicalId\":443433,\"journal\":{\"name\":\"IEEE International Conference on Vehicular Electronics and Safety, 2005.\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Vehicular Electronics and Safety, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICVES.2005.1563628\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Vehicular Electronics and Safety, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICVES.2005.1563628","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi-scale retinex algorithm for image enhancement
Rapidly and robust image enhancement transformation is important for numerous applications in intelligent video surveillance, which can improve the quality of scene image and extract better features in complex environment where the images undergo large lighting changes. In this paper, a multi-scale retinex algorithm is presented for image enhancement, which has two contributions. First, the initial approximation image is computed by both each pixel value and maximum value of original image. Second, discrete wavelet transformation is used to decrease computation complexity. Experimental tests on numerous scene images show that proposed algorithm has better performance for edge detecting from surveillance images and lane images.