{"title":"基于边缘增强全变分正则化的盲图像去模糊","authors":"Yu Shi, Hanyu Hong, Jie Song, Xia Hua","doi":"10.1117/12.2180852","DOIUrl":null,"url":null,"abstract":"Blind image deblurring is an important issue. In this paper, we focus on solving this issue by constrained regularization method. Motivated by the importance of edges to visual perception, the edge-enhancing indicator is introduced to constrain the total variation regularization, and the bilateral filter is used for edge-preserving smoothing. The proposed edge enhancing regularization method aims to smooth preferably within each region and preserve edges. Experiments on simulated and real motion blurred images show that the proposed method is competitive with recent state-of-the-art total variation methods.","PeriodicalId":225534,"journal":{"name":"Photoelectronic Technology Committee Conferences","volume":"19 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Blind image deblurring with edge enhancing total variation regularization\",\"authors\":\"Yu Shi, Hanyu Hong, Jie Song, Xia Hua\",\"doi\":\"10.1117/12.2180852\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Blind image deblurring is an important issue. In this paper, we focus on solving this issue by constrained regularization method. Motivated by the importance of edges to visual perception, the edge-enhancing indicator is introduced to constrain the total variation regularization, and the bilateral filter is used for edge-preserving smoothing. The proposed edge enhancing regularization method aims to smooth preferably within each region and preserve edges. Experiments on simulated and real motion blurred images show that the proposed method is competitive with recent state-of-the-art total variation methods.\",\"PeriodicalId\":225534,\"journal\":{\"name\":\"Photoelectronic Technology Committee Conferences\",\"volume\":\"19 3\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Photoelectronic Technology Committee Conferences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2180852\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Photoelectronic Technology Committee Conferences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2180852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Blind image deblurring with edge enhancing total variation regularization
Blind image deblurring is an important issue. In this paper, we focus on solving this issue by constrained regularization method. Motivated by the importance of edges to visual perception, the edge-enhancing indicator is introduced to constrain the total variation regularization, and the bilateral filter is used for edge-preserving smoothing. The proposed edge enhancing regularization method aims to smooth preferably within each region and preserve edges. Experiments on simulated and real motion blurred images show that the proposed method is competitive with recent state-of-the-art total variation methods.