迟滞阈值:一种基于图的小波块去噪算法

R. Ranta, V. Louis-Dorr
{"title":"迟滞阈值:一种基于图的小波块去噪算法","authors":"R. Ranta, V. Louis-Dorr","doi":"10.2174/1876825301003010006","DOIUrl":null,"url":null,"abstract":"This communication aims to combine several previously proposed wavelet denoising algorithms into a novel heuristic block method. The proposed ``hysteresis'' thresholding uses two thresholds simultaneously in order to combine detection and minimal alteration of informative features of the processed signal. This approach exploits the graph structure of the wavelet decomposition to detect clusters of significant wavelet coefficients. The new algorithm is compared with classical denoising methods on simulated benchmark signals.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Hysteresis Thresholding: A Graph-Based Wavelet Block Denoising Algorithm\",\"authors\":\"R. Ranta, V. Louis-Dorr\",\"doi\":\"10.2174/1876825301003010006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This communication aims to combine several previously proposed wavelet denoising algorithms into a novel heuristic block method. The proposed ``hysteresis'' thresholding uses two thresholds simultaneously in order to combine detection and minimal alteration of informative features of the processed signal. This approach exploits the graph structure of the wavelet decomposition to detect clusters of significant wavelet coefficients. The new algorithm is compared with classical denoising methods on simulated benchmark signals.\",\"PeriodicalId\":147157,\"journal\":{\"name\":\"The Open Signal Processing Journal\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Open Signal Processing Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1876825301003010006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Open Signal Processing Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1876825301003010006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

本文的目的是将先前提出的几种小波去噪算法结合成一种新的启发式块方法。所提出的“迟滞”阈值同时使用两个阈值,以便将检测和处理后信号信息特征的最小变化结合起来。该方法利用小波分解的图结构来检测具有显著小波系数的聚类。在模拟基准信号上与经典去噪方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hysteresis Thresholding: A Graph-Based Wavelet Block Denoising Algorithm
This communication aims to combine several previously proposed wavelet denoising algorithms into a novel heuristic block method. The proposed ``hysteresis'' thresholding uses two thresholds simultaneously in order to combine detection and minimal alteration of informative features of the processed signal. This approach exploits the graph structure of the wavelet decomposition to detect clusters of significant wavelet coefficients. The new algorithm is compared with classical denoising methods on simulated benchmark signals.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:481959085
Book学术官方微信