{"title":"基于空间自适应滤波的快速高效高斯噪声图像恢复算法","authors":"Tuan-Anh Nguyen, M. Kim, Min-Cheol Hong","doi":"10.1109/PCS.2010.5702438","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a spatially adaptive noise removal algorithm using local statistics that consists of two stages: noise detection and removal. To corporate desirable properties into denoising process, the local weighted mean, local weighted activity, and local maximum are defined. With these local statistics, the noise detection function is defined and a modified Gaussian filter is used to suppress the detected noise components. The experimental results demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":255142,"journal":{"name":"28th Picture Coding Symposium","volume":"271 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fast and efficient Gaussian noise image restoration algorithm by spatially adaptive filtering\",\"authors\":\"Tuan-Anh Nguyen, M. Kim, Min-Cheol Hong\",\"doi\":\"10.1109/PCS.2010.5702438\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a spatially adaptive noise removal algorithm using local statistics that consists of two stages: noise detection and removal. To corporate desirable properties into denoising process, the local weighted mean, local weighted activity, and local maximum are defined. With these local statistics, the noise detection function is defined and a modified Gaussian filter is used to suppress the detected noise components. The experimental results demonstrate the effectiveness of the proposed algorithm.\",\"PeriodicalId\":255142,\"journal\":{\"name\":\"28th Picture Coding Symposium\",\"volume\":\"271 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"28th Picture Coding Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PCS.2010.5702438\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"28th Picture Coding Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCS.2010.5702438","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast and efficient Gaussian noise image restoration algorithm by spatially adaptive filtering
In this paper, we propose a spatially adaptive noise removal algorithm using local statistics that consists of two stages: noise detection and removal. To corporate desirable properties into denoising process, the local weighted mean, local weighted activity, and local maximum are defined. With these local statistics, the noise detection function is defined and a modified Gaussian filter is used to suppress the detected noise components. The experimental results demonstrate the effectiveness of the proposed algorithm.