{"title":"图像中噪声的估计:一种评价","authors":"Olsen S.I.","doi":"10.1006/cgip.1993.1022","DOIUrl":null,"url":null,"abstract":"<div><p>Six methods for estimating the standard deviation of white additive noise in images are surveyed and evaluated experimentally by application to a set of images showing different degrees of contrast, edge details, texture, etc. The results show that on average, the most reliable estimate is obtained by prefiltering the image to suppress the image structure and then computing the standard deviation value from the filtered data.</p></div>","PeriodicalId":100349,"journal":{"name":"CVGIP: Graphical Models and Image Processing","volume":"55 4","pages":"Pages 319-323"},"PeriodicalIF":0.0000,"publicationDate":"1993-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/cgip.1993.1022","citationCount":"274","resultStr":"{\"title\":\"Estimation of Noise in Images: An Evaluation\",\"authors\":\"Olsen S.I.\",\"doi\":\"10.1006/cgip.1993.1022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Six methods for estimating the standard deviation of white additive noise in images are surveyed and evaluated experimentally by application to a set of images showing different degrees of contrast, edge details, texture, etc. The results show that on average, the most reliable estimate is obtained by prefiltering the image to suppress the image structure and then computing the standard deviation value from the filtered data.</p></div>\",\"PeriodicalId\":100349,\"journal\":{\"name\":\"CVGIP: Graphical Models and Image Processing\",\"volume\":\"55 4\",\"pages\":\"Pages 319-323\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1993-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1006/cgip.1993.1022\",\"citationCount\":\"274\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CVGIP: Graphical Models and Image Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1049965283710229\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CVGIP: Graphical Models and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1049965283710229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Six methods for estimating the standard deviation of white additive noise in images are surveyed and evaluated experimentally by application to a set of images showing different degrees of contrast, edge details, texture, etc. The results show that on average, the most reliable estimate is obtained by prefiltering the image to suppress the image structure and then computing the standard deviation value from the filtered data.
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