{"title":"Natural Scene Statistics for Noise Estimation","authors":"Praful Gupta, C. Bampis, Yize Jin, A. Bovik","doi":"10.1109/SSIAI.2018.8470313","DOIUrl":null,"url":null,"abstract":"We investigate the scale-invariant properties of divisively normalized bandpass responses of natural images in the DCT-filtered domain. We found that the variance of the normalized DCT filtered responses of a pristine natural image is scale invariant. This scale invariance property does not hold in the presence of noise and thus it can be used to devise an efficient blind image noise estimator. The proposed noise estimation approach outperforms other statistics-based methods especially for higher noise levels and competes well with patch-based and filter-based approaches. Moreover, the new variance estimation approach is also effective in the case of non-Gaussian noise. The research code of the proposed algorithm can be found at https://github.com/guptapraful/Noise Estimation.","PeriodicalId":422209,"journal":{"name":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSIAI.2018.8470313","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
We investigate the scale-invariant properties of divisively normalized bandpass responses of natural images in the DCT-filtered domain. We found that the variance of the normalized DCT filtered responses of a pristine natural image is scale invariant. This scale invariance property does not hold in the presence of noise and thus it can be used to devise an efficient blind image noise estimator. The proposed noise estimation approach outperforms other statistics-based methods especially for higher noise levels and competes well with patch-based and filter-based approaches. Moreover, the new variance estimation approach is also effective in the case of non-Gaussian noise. The research code of the proposed algorithm can be found at https://github.com/guptapraful/Noise Estimation.