{"title":"一种基于BEMD的探地雷达图像去噪方法","authors":"Lu Gan, Long Zhou, Xinge You","doi":"10.1109/SPAC.2014.6982709","DOIUrl":null,"url":null,"abstract":"This paper presents a new de-noising method for GPR image based on BEMD and wavelet. This method complies with the adaptability from BEMD. The method decomposes the image into a series of IMF components, then applies wavelet threshold de-noising on the selected high frequency IMF components for de-noising. In the reconstruction course, the de-noising IMF and low frequency IMF are combined. The experiment results shows the effectiveness of the method on GPR image.","PeriodicalId":326246,"journal":{"name":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"65 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A new GPR image de-nosing method based on BEMD\",\"authors\":\"Lu Gan, Long Zhou, Xinge You\",\"doi\":\"10.1109/SPAC.2014.6982709\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new de-noising method for GPR image based on BEMD and wavelet. This method complies with the adaptability from BEMD. The method decomposes the image into a series of IMF components, then applies wavelet threshold de-noising on the selected high frequency IMF components for de-noising. In the reconstruction course, the de-noising IMF and low frequency IMF are combined. The experiment results shows the effectiveness of the method on GPR image.\",\"PeriodicalId\":326246,\"journal\":{\"name\":\"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)\",\"volume\":\"65 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPAC.2014.6982709\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAC.2014.6982709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a new de-noising method for GPR image based on BEMD and wavelet. This method complies with the adaptability from BEMD. The method decomposes the image into a series of IMF components, then applies wavelet threshold de-noising on the selected high frequency IMF components for de-noising. In the reconstruction course, the de-noising IMF and low frequency IMF are combined. The experiment results shows the effectiveness of the method on GPR image.
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