{"title":"改进的小波阈值鲁棒图像去噪方法","authors":"Hong Zhang, Hui Liu, Zhenhong Shang, Ruixin Li","doi":"10.1109/ICSESS.2015.7339060","DOIUrl":null,"url":null,"abstract":"This paper proposed an improved threshold function and threshold estimation method for analyzing the characteristics of the wavelet threshold denoising. Because hard threshold denoising makes boundaries fuzzy and soft threshold denoising has Gibbs phenomenon, the new threshold function and estimation method have better adaptive. Results show that the improved method can effectively remove the white noise, the improved method is better than the soft, hard threshold denoising.","PeriodicalId":335871,"journal":{"name":"2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Robust image denoising with an improved wavelet threshold method\",\"authors\":\"Hong Zhang, Hui Liu, Zhenhong Shang, Ruixin Li\",\"doi\":\"10.1109/ICSESS.2015.7339060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed an improved threshold function and threshold estimation method for analyzing the characteristics of the wavelet threshold denoising. Because hard threshold denoising makes boundaries fuzzy and soft threshold denoising has Gibbs phenomenon, the new threshold function and estimation method have better adaptive. Results show that the improved method can effectively remove the white noise, the improved method is better than the soft, hard threshold denoising.\",\"PeriodicalId\":335871,\"journal\":{\"name\":\"2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSESS.2015.7339060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSESS.2015.7339060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust image denoising with an improved wavelet threshold method
This paper proposed an improved threshold function and threshold estimation method for analyzing the characteristics of the wavelet threshold denoising. Because hard threshold denoising makes boundaries fuzzy and soft threshold denoising has Gibbs phenomenon, the new threshold function and estimation method have better adaptive. Results show that the improved method can effectively remove the white noise, the improved method is better than the soft, hard threshold denoising.