Anupama Shetter, S. N. Prajwalasimha, Swapna Havalgi
{"title":"基于滤波和直方图均衡化的图像去噪算法","authors":"Anupama Shetter, S. N. Prajwalasimha, Swapna Havalgi","doi":"10.1109/I-SMAC.2018.8653714","DOIUrl":null,"url":null,"abstract":"In this paper, a collective median filtering and histogram equalization based de-noising technique is proposed for images. Initial noise detection is performed by considering neighboring pixel values then median filtering is performed to remove high density noise. The filtered image is then subjected for histogram equalization to regain correlation between adjacent pixels. The final image enhancement is done by contrast adjustment method. The experimental results show that the proposed algorithm provides high quality restored images compared to existing ones.","PeriodicalId":53631,"journal":{"name":"Koomesh","volume":"41 1","pages":"325-328"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Image De-Noising Algorithm based on Filtering and Histogram Equalization\",\"authors\":\"Anupama Shetter, S. N. Prajwalasimha, Swapna Havalgi\",\"doi\":\"10.1109/I-SMAC.2018.8653714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a collective median filtering and histogram equalization based de-noising technique is proposed for images. Initial noise detection is performed by considering neighboring pixel values then median filtering is performed to remove high density noise. The filtered image is then subjected for histogram equalization to regain correlation between adjacent pixels. The final image enhancement is done by contrast adjustment method. The experimental results show that the proposed algorithm provides high quality restored images compared to existing ones.\",\"PeriodicalId\":53631,\"journal\":{\"name\":\"Koomesh\",\"volume\":\"41 1\",\"pages\":\"325-328\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Koomesh\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I-SMAC.2018.8653714\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Koomesh","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I-SMAC.2018.8653714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Image De-Noising Algorithm based on Filtering and Histogram Equalization
In this paper, a collective median filtering and histogram equalization based de-noising technique is proposed for images. Initial noise detection is performed by considering neighboring pixel values then median filtering is performed to remove high density noise. The filtered image is then subjected for histogram equalization to regain correlation between adjacent pixels. The final image enhancement is done by contrast adjustment method. The experimental results show that the proposed algorithm provides high quality restored images compared to existing ones.