{"title":"实时视频去噪的自适应模糊滤波算法","authors":"Jing Wu, Xin Du, Yunfang Zhu, Gu Wei-kang","doi":"10.1109/ICOSP.2008.4697367","DOIUrl":null,"url":null,"abstract":"In order to improve the perceptual quality and the compression efficiency in video transition, a new adaptive fuzzy filter algorithm for real-time Gaussian noise removal is proposed. Based on the analysis of noise and video properties, it exploits the spatiotemporal neighbor information through noise estimation, motion detection, and spatiotemporal neighborspsila similarity measurement. Then, an adaptive fuzzy filter is applied to remove noise. Experimental results showed that the proposed method achieved better performances of noise removal and detail preservation. Compared with other methods, PSNR of the denoised video was improved 1~ 6 dB. And, benefited from its low complexity and fast computation, it could meet the real-time requirements of the video transition.","PeriodicalId":445699,"journal":{"name":"2008 9th International Conference on Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Adaptive fuzzy filter algorithm for real-time video denoising\",\"authors\":\"Jing Wu, Xin Du, Yunfang Zhu, Gu Wei-kang\",\"doi\":\"10.1109/ICOSP.2008.4697367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the perceptual quality and the compression efficiency in video transition, a new adaptive fuzzy filter algorithm for real-time Gaussian noise removal is proposed. Based on the analysis of noise and video properties, it exploits the spatiotemporal neighbor information through noise estimation, motion detection, and spatiotemporal neighborspsila similarity measurement. Then, an adaptive fuzzy filter is applied to remove noise. Experimental results showed that the proposed method achieved better performances of noise removal and detail preservation. Compared with other methods, PSNR of the denoised video was improved 1~ 6 dB. And, benefited from its low complexity and fast computation, it could meet the real-time requirements of the video transition.\",\"PeriodicalId\":445699,\"journal\":{\"name\":\"2008 9th International Conference on Signal Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 9th International Conference on Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.2008.4697367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 9th International Conference on Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.2008.4697367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive fuzzy filter algorithm for real-time video denoising
In order to improve the perceptual quality and the compression efficiency in video transition, a new adaptive fuzzy filter algorithm for real-time Gaussian noise removal is proposed. Based on the analysis of noise and video properties, it exploits the spatiotemporal neighbor information through noise estimation, motion detection, and spatiotemporal neighborspsila similarity measurement. Then, an adaptive fuzzy filter is applied to remove noise. Experimental results showed that the proposed method achieved better performances of noise removal and detail preservation. Compared with other methods, PSNR of the denoised video was improved 1~ 6 dB. And, benefited from its low complexity and fast computation, it could meet the real-time requirements of the video transition.