{"title":"基于微分进化的自适应收缩函数优化","authors":"K. K. Gupta, R. Gupta","doi":"10.1109/ADCOM.2007.31","DOIUrl":null,"url":null,"abstract":"In this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The features are detected by energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. The shrinkage function is optimized by differential Evolution (DE).","PeriodicalId":185608,"journal":{"name":"15th International Conference on Advanced Computing and Communications (ADCOM 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Shrinkage Function Optimization by Differential Evolution\",\"authors\":\"K. K. Gupta, R. Gupta\",\"doi\":\"10.1109/ADCOM.2007.31\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The features are detected by energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. The shrinkage function is optimized by differential Evolution (DE).\",\"PeriodicalId\":185608,\"journal\":{\"name\":\"15th International Conference on Advanced Computing and Communications (ADCOM 2007)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"15th International Conference on Advanced Computing and Communications (ADCOM 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ADCOM.2007.31\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"15th International Conference on Advanced Computing and Communications (ADCOM 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ADCOM.2007.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Shrinkage Function Optimization by Differential Evolution
In this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The features are detected by energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. The shrinkage function is optimized by differential Evolution (DE).