{"title":"随机测量重建","authors":"M. Kayvanrad","doi":"10.1109/ICOSP.2008.4697705","DOIUrl":null,"url":null,"abstract":"A practical method of reconstruction of signals from a small number of random observations is put forward. The method takes advantage of the sparsity of the signal in wavelet domain to reconstruct it in an iterative manner. The proposed method is shown to be quite successful in reconstruction of 1D as well as 2D signals from a few numbers of randomly acquired samples. It also proves to be robust to observation noise.","PeriodicalId":445699,"journal":{"name":"2008 9th International Conference on Signal Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Reconstruction from random measurements\",\"authors\":\"M. Kayvanrad\",\"doi\":\"10.1109/ICOSP.2008.4697705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A practical method of reconstruction of signals from a small number of random observations is put forward. The method takes advantage of the sparsity of the signal in wavelet domain to reconstruct it in an iterative manner. The proposed method is shown to be quite successful in reconstruction of 1D as well as 2D signals from a few numbers of randomly acquired samples. It also proves to be robust to observation noise.\",\"PeriodicalId\":445699,\"journal\":{\"name\":\"2008 9th International Conference on Signal Processing\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 9th International Conference on Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.2008.4697705\",\"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.2008.4697705","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A practical method of reconstruction of signals from a small number of random observations is put forward. The method takes advantage of the sparsity of the signal in wavelet domain to reconstruct it in an iterative manner. The proposed method is shown to be quite successful in reconstruction of 1D as well as 2D signals from a few numbers of randomly acquired samples. It also proves to be robust to observation noise.
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