Adaptive Block-Based Singular Value Decomposition Filtering

Napa Sae-Bae, S. Udomhunsakul
{"title":"Adaptive Block-Based Singular Value Decomposition Filtering","authors":"Napa Sae-Bae, S. Udomhunsakul","doi":"10.1109/CGIV.2007.15","DOIUrl":null,"url":null,"abstract":"Noise reduction is one of the most important processes to enhance the quality of reconstructed image. In this paper, we present an adaptive block-based singular value decomposition method for noise reduction. Instead of applying block-based singular value decomposition (BSVD) directly to noisy images, we propose to apply BSVD on the noisy edge image version obtained from the difference between the original noisy image and its blur image version. From the experimental results, we demonstrate that our proposed approach compared with traditionally methods can remove noise, preserve edges as well as effectively smooth in the homogenous region. Therefore, our method leads to a practical method to be used for noise reduction.","PeriodicalId":433577,"journal":{"name":"Computer Graphics, Imaging and Visualisation (CGIV 2007)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Graphics, Imaging and Visualisation (CGIV 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CGIV.2007.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

Noise reduction is one of the most important processes to enhance the quality of reconstructed image. In this paper, we present an adaptive block-based singular value decomposition method for noise reduction. Instead of applying block-based singular value decomposition (BSVD) directly to noisy images, we propose to apply BSVD on the noisy edge image version obtained from the difference between the original noisy image and its blur image version. From the experimental results, we demonstrate that our proposed approach compared with traditionally methods can remove noise, preserve edges as well as effectively smooth in the homogenous region. Therefore, our method leads to a practical method to be used for noise reduction.
基于块的自适应奇异值分解滤波
降噪是提高重建图像质量的重要步骤之一。本文提出了一种基于块的自适应奇异值分解降噪方法。我们提出将基于块的奇异值分解(BSVD)应用于原始噪声图像与其模糊图像的差值得到的噪声边缘图像版本,而不是直接应用于噪声图像。实验结果表明,与传统方法相比,该方法可以有效地去除噪声,保持边缘,并在均匀区域内进行平滑处理。因此,我们的方法为降噪提供了一种实用的方法。
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