{"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}
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.