{"title":"基于空间噪声估计的小波图像去噪","authors":"Souad Benabdelkader, Ouarda Soltani","doi":"10.1109/SPIS.2015.7422317","DOIUrl":null,"url":null,"abstract":"The classical wavelet denoising scheme estimates the noise level in the wavelet domain using only the upper detail subband. In this paper, we present a hybrid method for wavelet image denoising in which the standard deviation of the noise is estimated on the entire image pixels in the spatial domain within an adaptive edge preservation scheme. Thereafter, that estimation is used to calculate the threshold for wavelet coefficients shrinkage.","PeriodicalId":424434,"journal":{"name":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Wavelet image denoising based spatial noise estimation\",\"authors\":\"Souad Benabdelkader, Ouarda Soltani\",\"doi\":\"10.1109/SPIS.2015.7422317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The classical wavelet denoising scheme estimates the noise level in the wavelet domain using only the upper detail subband. In this paper, we present a hybrid method for wavelet image denoising in which the standard deviation of the noise is estimated on the entire image pixels in the spatial domain within an adaptive edge preservation scheme. Thereafter, that estimation is used to calculate the threshold for wavelet coefficients shrinkage.\",\"PeriodicalId\":424434,\"journal\":{\"name\":\"2015 Signal Processing and Intelligent Systems Conference (SPIS)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Signal Processing and Intelligent Systems Conference (SPIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPIS.2015.7422317\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPIS.2015.7422317","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wavelet image denoising based spatial noise estimation
The classical wavelet denoising scheme estimates the noise level in the wavelet domain using only the upper detail subband. In this paper, we present a hybrid method for wavelet image denoising in which the standard deviation of the noise is estimated on the entire image pixels in the spatial domain within an adaptive edge preservation scheme. Thereafter, that estimation is used to calculate the threshold for wavelet coefficients shrinkage.
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