{"title":"模拟退火和基于局部对比度的各向异性扩散1/f降噪方法","authors":"Deng Zhang, J. Ryu, T. Nishimura","doi":"10.1109/ICACIA.2009.5361091","DOIUrl":null,"url":null,"abstract":"Anisotropic diffusion based de-noising methods have been demonstrated for the effectiveness on both noise suppression and edge preservation. However, pulse noise liked spots in the de-noised images and threshold selection are two problems of these methods. This paper presents a simulated annealing and local contrast based anisotropic diffusion method for 1/f noise reduction on the pinned-type complementary metal oxide semiconductor image sensors (CMOS image sensors: CIS). Experimental results reveal that the proposed method is an acceptably good solution to the avoidance of the appearance of the pulse noise liked spots in the de-noised images and the threshold selection.","PeriodicalId":423210,"journal":{"name":"2009 International Conference on Apperceiving Computing and Intelligence Analysis","volume":"129 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simulated annealing and local contrast based anisotropic diffusion method for 1/f noise reduction\",\"authors\":\"Deng Zhang, J. Ryu, T. Nishimura\",\"doi\":\"10.1109/ICACIA.2009.5361091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Anisotropic diffusion based de-noising methods have been demonstrated for the effectiveness on both noise suppression and edge preservation. However, pulse noise liked spots in the de-noised images and threshold selection are two problems of these methods. This paper presents a simulated annealing and local contrast based anisotropic diffusion method for 1/f noise reduction on the pinned-type complementary metal oxide semiconductor image sensors (CMOS image sensors: CIS). Experimental results reveal that the proposed method is an acceptably good solution to the avoidance of the appearance of the pulse noise liked spots in the de-noised images and the threshold selection.\",\"PeriodicalId\":423210,\"journal\":{\"name\":\"2009 International Conference on Apperceiving Computing and Intelligence Analysis\",\"volume\":\"129 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Apperceiving Computing and Intelligence Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACIA.2009.5361091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Apperceiving Computing and Intelligence Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACIA.2009.5361091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simulated annealing and local contrast based anisotropic diffusion method for 1/f noise reduction
Anisotropic diffusion based de-noising methods have been demonstrated for the effectiveness on both noise suppression and edge preservation. However, pulse noise liked spots in the de-noised images and threshold selection are two problems of these methods. This paper presents a simulated annealing and local contrast based anisotropic diffusion method for 1/f noise reduction on the pinned-type complementary metal oxide semiconductor image sensors (CMOS image sensors: CIS). Experimental results reveal that the proposed method is an acceptably good solution to the avoidance of the appearance of the pulse noise liked spots in the de-noised images and the threshold selection.