A robust image reconstruction based on convex combination of criteria

Y. Xia, Wenyao Xia
{"title":"A robust image reconstruction based on convex combination of criteria","authors":"Y. Xia, Wenyao Xia","doi":"10.1109/CISP.2015.7407998","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel regularization method for robust image reconstruction against noise, based on convex combination of the least squares and least absolute deviations. Unlike conventional regularization methods with an assumption of Guaussian noise, the proposed regularization method can deal with Gaussian noise and non-Gaussian noise. To overcome difficulty of the non-smooth objective function, we develop an efficient sub-gradient algorithm. Computed examples with an application to MR images show that the proposed subgradient algorithm can give better reconstruction quality than the conventional reconstruction regularization algorithms in various noise.","PeriodicalId":167631,"journal":{"name":"2015 8th International Congress on Image and Signal Processing (CISP)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Congress on Image and Signal Processing (CISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISP.2015.7407998","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper we propose a novel regularization method for robust image reconstruction against noise, based on convex combination of the least squares and least absolute deviations. Unlike conventional regularization methods with an assumption of Guaussian noise, the proposed regularization method can deal with Gaussian noise and non-Gaussian noise. To overcome difficulty of the non-smooth objective function, we develop an efficient sub-gradient algorithm. Computed examples with an application to MR images show that the proposed subgradient algorithm can give better reconstruction quality than the conventional reconstruction regularization algorithms in various noise.
基于凸组合准则的鲁棒图像重建
本文提出了一种基于最小二乘和最小绝对偏差的凸组合的鲁棒图像重构方法。与传统的正则化方法假设高斯噪声不同,本文提出的正则化方法可以处理高斯噪声和非高斯噪声。为了克服目标函数不光滑的困难,我们开发了一种高效的子梯度算法。应用于磁共振图像的算例表明,在各种噪声条件下,该算法比传统的重构正则化算法具有更好的重构质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信