{"title":"非局部意味着使用自适应核去噪","authors":"A. Tahmouresi, S. Saryazdi, S. Seydnejad","doi":"10.1109/IRANIANCEE.2012.6292584","DOIUrl":null,"url":null,"abstract":"Non-local means algorithm is one of the powerful image denoising methods. Maintaining noise near edges and textural parts of a noisy image, is one of the main drawbacks of NLM. In this paper we introduce an adaptive kernel derived from image structure to remove maintained noise. Experimental results show superiority of our algorithm in comparison with original NLM as well as a method based on shape adaptive patches.","PeriodicalId":308726,"journal":{"name":"20th Iranian Conference on Electrical Engineering (ICEE2012)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Non-local means denoising using an adaptive kernel\",\"authors\":\"A. Tahmouresi, S. Saryazdi, S. Seydnejad\",\"doi\":\"10.1109/IRANIANCEE.2012.6292584\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-local means algorithm is one of the powerful image denoising methods. Maintaining noise near edges and textural parts of a noisy image, is one of the main drawbacks of NLM. In this paper we introduce an adaptive kernel derived from image structure to remove maintained noise. Experimental results show superiority of our algorithm in comparison with original NLM as well as a method based on shape adaptive patches.\",\"PeriodicalId\":308726,\"journal\":{\"name\":\"20th Iranian Conference on Electrical Engineering (ICEE2012)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"20th Iranian Conference on Electrical Engineering (ICEE2012)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRANIANCEE.2012.6292584\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"20th Iranian Conference on Electrical Engineering (ICEE2012)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRANIANCEE.2012.6292584","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-local means denoising using an adaptive kernel
Non-local means algorithm is one of the powerful image denoising methods. Maintaining noise near edges and textural parts of a noisy image, is one of the main drawbacks of NLM. In this paper we introduce an adaptive kernel derived from image structure to remove maintained noise. Experimental results show superiority of our algorithm in comparison with original NLM as well as a method based on shape adaptive patches.
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