{"title":"基于pde的正则化方法与统一框架的比较","authors":"Bochao Su, Xiaohua Zhang, Wanyu Liu, Li Li","doi":"10.1109/IMCCC.2014.114","DOIUrl":null,"url":null,"abstract":"The frequent problems in computer vision consist of de-noising, artifact elimination as well as structure preserving or enhancing. PDE-based nonlinear diffusion filter may be one possibility to achieve those goals. In this paper, we perform comparison of three typical PDE-based regularization algorithms followed by the proposal of a general framework, which exploits fundamental significance for analyzing PDE-based regularization methods.","PeriodicalId":152074,"journal":{"name":"2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of the PDE-Based Regularization Methods and a Unifying Framework\",\"authors\":\"Bochao Su, Xiaohua Zhang, Wanyu Liu, Li Li\",\"doi\":\"10.1109/IMCCC.2014.114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The frequent problems in computer vision consist of de-noising, artifact elimination as well as structure preserving or enhancing. PDE-based nonlinear diffusion filter may be one possibility to achieve those goals. In this paper, we perform comparison of three typical PDE-based regularization algorithms followed by the proposal of a general framework, which exploits fundamental significance for analyzing PDE-based regularization methods.\",\"PeriodicalId\":152074,\"journal\":{\"name\":\"2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMCCC.2014.114\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMCCC.2014.114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of the PDE-Based Regularization Methods and a Unifying Framework
The frequent problems in computer vision consist of de-noising, artifact elimination as well as structure preserving or enhancing. PDE-based nonlinear diffusion filter may be one possibility to achieve those goals. In this paper, we perform comparison of three typical PDE-based regularization algorithms followed by the proposal of a general framework, which exploits fundamental significance for analyzing PDE-based regularization methods.
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