{"title":"形状保持的非凸正则化","authors":"R. Chartrand","doi":"10.1109/ICIP.2007.4378949","DOIUrl":null,"url":null,"abstract":"We show that using a nonconvex penalty term to regularize image reconstruction can substantially improve the preservation of object shapes. The commonly-used total-variation regularization, int |nablau|, penalizes the length of object edges. We show that int |nablau|p, 0 < p < 1, only penalizes edges of dimension at least 2 - p, and thus finite-length edges not at all. We give numerical examples showing the resulting improvement in shape preservation.","PeriodicalId":131177,"journal":{"name":"2007 IEEE International Conference on Image Processing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"Nonconvex Regularization for Shape Preservation\",\"authors\":\"R. Chartrand\",\"doi\":\"10.1109/ICIP.2007.4378949\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We show that using a nonconvex penalty term to regularize image reconstruction can substantially improve the preservation of object shapes. The commonly-used total-variation regularization, int |nablau|, penalizes the length of object edges. We show that int |nablau|p, 0 < p < 1, only penalizes edges of dimension at least 2 - p, and thus finite-length edges not at all. We give numerical examples showing the resulting improvement in shape preservation.\",\"PeriodicalId\":131177,\"journal\":{\"name\":\"2007 IEEE International Conference on Image Processing\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Conference on Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2007.4378949\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Conference on Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2007.4378949","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 42
摘要
我们证明了使用非凸惩罚项来正则化图像重建可以大大提高物体形状的保存。常用的全变分正则化(int |nablau)惩罚对象边的长度。我们证明了int |nablau|p, 0 < p < 1,只惩罚维度至少为2 - p的边,因此不惩罚有限长边。我们给出了数值例子来证明在形状保存方面的改进。
We show that using a nonconvex penalty term to regularize image reconstruction can substantially improve the preservation of object shapes. The commonly-used total-variation regularization, int |nablau|, penalizes the length of object edges. We show that int |nablau|p, 0 < p < 1, only penalizes edges of dimension at least 2 - p, and thus finite-length edges not at all. We give numerical examples showing the resulting improvement in shape preservation.