{"title":"一种快速、自适应的图像对比度增强方法","authors":"Zeyun Yu, C. Bajaj","doi":"10.1109/ICIP.2004.1419470","DOIUrl":null,"url":null,"abstract":"In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.","PeriodicalId":184798,"journal":{"name":"2004 International Conference on Image Processing, 2004. ICIP '04.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"134","resultStr":"{\"title\":\"A fast and adaptive method for image contrast enhancement\",\"authors\":\"Zeyun Yu, C. Bajaj\",\"doi\":\"10.1109/ICIP.2004.1419470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.\",\"PeriodicalId\":184798,\"journal\":{\"name\":\"2004 International Conference on Image Processing, 2004. ICIP '04.\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"134\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 International Conference on Image Processing, 2004. ICIP '04.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.2004.1419470\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 International Conference on Image Processing, 2004. ICIP '04.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2004.1419470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fast and adaptive method for image contrast enhancement
In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only last and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.
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