{"title":"使用模糊逻辑增强图像的视觉质量","authors":"T. Aarthi, E. Sowmiya, N. Sairam","doi":"10.1109/ISCO.2014.7103952","DOIUrl":null,"url":null,"abstract":"Image enhancement techniques are used to alter the intensity value of an image in order to improve the visual quality. Many popular enhancement techniques fail to work in some applications due to background noise generated in the image. To address this problem, we propose a fuzzy enhancement technique. In this technique, the gray scale image is fuzzified and it is defuzzified after changing its membership values. Experimental results are shown for different gray scale images along with its performance.","PeriodicalId":119329,"journal":{"name":"2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Enhancement of visual quality of an image using fuzzy logic\",\"authors\":\"T. Aarthi, E. Sowmiya, N. Sairam\",\"doi\":\"10.1109/ISCO.2014.7103952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image enhancement techniques are used to alter the intensity value of an image in order to improve the visual quality. Many popular enhancement techniques fail to work in some applications due to background noise generated in the image. To address this problem, we propose a fuzzy enhancement technique. In this technique, the gray scale image is fuzzified and it is defuzzified after changing its membership values. Experimental results are shown for different gray scale images along with its performance.\",\"PeriodicalId\":119329,\"journal\":{\"name\":\"2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCO.2014.7103952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCO.2014.7103952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhancement of visual quality of an image using fuzzy logic
Image enhancement techniques are used to alter the intensity value of an image in order to improve the visual quality. Many popular enhancement techniques fail to work in some applications due to background noise generated in the image. To address this problem, we propose a fuzzy enhancement technique. In this technique, the gray scale image is fuzzified and it is defuzzified after changing its membership values. Experimental results are shown for different gray scale images along with its performance.