{"title":"稀疏度蒸馏图像去噪","authors":"S. Kawata, Nao Mishima","doi":"10.2197/ipsjtcva.7.50","DOIUrl":null,"url":null,"abstract":"We propose a new image denoising method with shrinkage. In the proposed method, small blocks in an input image are projected to the space that makes projection coefficients sparse, and the explicitly evaluated sparsity degree is used to control the shrinkage threshold. On average, the proposed method obtained higher quantitative evaluation values (PSNRs and SSIMs) compared with one of the state-of-the-art methods in the field of image denoising. The proposed method removes random noise effectively from natural images while preserving intricate textures.","PeriodicalId":38957,"journal":{"name":"IPSJ Transactions on Computer Vision and Applications","volume":"40 1","pages":"50-54"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Image Denoising with Sparsity Distillation\",\"authors\":\"S. Kawata, Nao Mishima\",\"doi\":\"10.2197/ipsjtcva.7.50\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a new image denoising method with shrinkage. In the proposed method, small blocks in an input image are projected to the space that makes projection coefficients sparse, and the explicitly evaluated sparsity degree is used to control the shrinkage threshold. On average, the proposed method obtained higher quantitative evaluation values (PSNRs and SSIMs) compared with one of the state-of-the-art methods in the field of image denoising. The proposed method removes random noise effectively from natural images while preserving intricate textures.\",\"PeriodicalId\":38957,\"journal\":{\"name\":\"IPSJ Transactions on Computer Vision and Applications\",\"volume\":\"40 1\",\"pages\":\"50-54\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IPSJ Transactions on Computer Vision and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2197/ipsjtcva.7.50\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSJ Transactions on Computer Vision and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2197/ipsjtcva.7.50","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
We propose a new image denoising method with shrinkage. In the proposed method, small blocks in an input image are projected to the space that makes projection coefficients sparse, and the explicitly evaluated sparsity degree is used to control the shrinkage threshold. On average, the proposed method obtained higher quantitative evaluation values (PSNRs and SSIMs) compared with one of the state-of-the-art methods in the field of image denoising. The proposed method removes random noise effectively from natural images while preserving intricate textures.