{"title":"基于二进小波变换的独立分量分析图像去噪","authors":"Zhenghong Huang","doi":"10.1109/ICWAPR.2009.5207412","DOIUrl":null,"url":null,"abstract":"Based on the dyadic wavelet transform, the threshold and threshold function are obtained adaptive with the decomposition of the dyadic wavelet coefficient by to improve of the lower bound error the noise threshold, and layered processing for threshold function. The noise mixed image was separated denoising by independent component analysis. Experiments show that the proposed method improves the signal-to-noise rate. Moreover, It's better the image precision.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Image denoising by independent component analysis based on dyadic wavelet transform\",\"authors\":\"Zhenghong Huang\",\"doi\":\"10.1109/ICWAPR.2009.5207412\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the dyadic wavelet transform, the threshold and threshold function are obtained adaptive with the decomposition of the dyadic wavelet coefficient by to improve of the lower bound error the noise threshold, and layered processing for threshold function. The noise mixed image was separated denoising by independent component analysis. Experiments show that the proposed method improves the signal-to-noise rate. Moreover, It's better the image precision.\",\"PeriodicalId\":424264,\"journal\":{\"name\":\"2009 International Conference on Wavelet Analysis and Pattern Recognition\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Wavelet Analysis and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICWAPR.2009.5207412\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Wavelet Analysis and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWAPR.2009.5207412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image denoising by independent component analysis based on dyadic wavelet transform
Based on the dyadic wavelet transform, the threshold and threshold function are obtained adaptive with the decomposition of the dyadic wavelet coefficient by to improve of the lower bound error the noise threshold, and layered processing for threshold function. The noise mixed image was separated denoising by independent component analysis. Experiments show that the proposed method improves the signal-to-noise rate. Moreover, It's better the image precision.
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