{"title":"基于复小波变换和奇异值分解的图像对比度增强","authors":"H. Demirel, G. Anbarjafari","doi":"10.1109/SIU.2010.5652001","DOIUrl":null,"url":null,"abstract":"In this work, we have proposed a new image contrast enhancement technique based on complex wavelet transform (CWT) and singular value decomposition (SVD). The technique decomposes the input image into the eight frequency subbands by using CWT and estimates the singular value matrix of the real and complex low-low subbands, and then it reconstructs the enhanced image by applying the inverse CWT (ICWT). The technique is compared with the conventional image equalization techniques such as standard general histogram equalization (GHE) and local histogram equalization (LHE), as well as state-of-art technique such as Brightness Preserving Dynamic Histogram Equalization (BPDHE) and singular value equalization (SVE). The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques.","PeriodicalId":152297,"journal":{"name":"2010 IEEE 18th Signal Processing and Communications Applications Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Complex wavelet transform and singular value decomposition based image contrast enhancement\",\"authors\":\"H. Demirel, G. Anbarjafari\",\"doi\":\"10.1109/SIU.2010.5652001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we have proposed a new image contrast enhancement technique based on complex wavelet transform (CWT) and singular value decomposition (SVD). The technique decomposes the input image into the eight frequency subbands by using CWT and estimates the singular value matrix of the real and complex low-low subbands, and then it reconstructs the enhanced image by applying the inverse CWT (ICWT). The technique is compared with the conventional image equalization techniques such as standard general histogram equalization (GHE) and local histogram equalization (LHE), as well as state-of-art technique such as Brightness Preserving Dynamic Histogram Equalization (BPDHE) and singular value equalization (SVE). The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques.\",\"PeriodicalId\":152297,\"journal\":{\"name\":\"2010 IEEE 18th Signal Processing and Communications Applications Conference\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 18th Signal Processing and Communications Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2010.5652001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 18th Signal Processing and Communications Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2010.5652001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Complex wavelet transform and singular value decomposition based image contrast enhancement
In this work, we have proposed a new image contrast enhancement technique based on complex wavelet transform (CWT) and singular value decomposition (SVD). The technique decomposes the input image into the eight frequency subbands by using CWT and estimates the singular value matrix of the real and complex low-low subbands, and then it reconstructs the enhanced image by applying the inverse CWT (ICWT). The technique is compared with the conventional image equalization techniques such as standard general histogram equalization (GHE) and local histogram equalization (LHE), as well as state-of-art technique such as Brightness Preserving Dynamic Histogram Equalization (BPDHE) and singular value equalization (SVE). The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques.