{"title":"基于k -最优正交匹配追踪的压缩感知","authors":"Pu-Hsuan Lin, S. Tsai, G. C. Chuang","doi":"10.1109/ICASSP.2013.6638757","DOIUrl":null,"url":null,"abstract":"This paper proposes an orthogonal matching pursuit (OMP-) based recovering algorithm for compressive sensing problems. This algorithm can significantly improve recovering performance while it can still maintain reasonable computational complexity. Complexity analysis and simulation results are provided for the proposed algorithm and compared with other popular recovering schemes. We observe that the proposed algorithm can significantly improve the exact recovering performance compared to the OMP scheme. Moreover, in the cases with high compressed ratio, the proposed algorithm can even outperform the benchmark performance achieved by the subspace programming and linear programming.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"56 3-4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A K-best orthogonal matching pursuit for compressive sensing\",\"authors\":\"Pu-Hsuan Lin, S. Tsai, G. C. Chuang\",\"doi\":\"10.1109/ICASSP.2013.6638757\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an orthogonal matching pursuit (OMP-) based recovering algorithm for compressive sensing problems. This algorithm can significantly improve recovering performance while it can still maintain reasonable computational complexity. Complexity analysis and simulation results are provided for the proposed algorithm and compared with other popular recovering schemes. We observe that the proposed algorithm can significantly improve the exact recovering performance compared to the OMP scheme. Moreover, in the cases with high compressed ratio, the proposed algorithm can even outperform the benchmark performance achieved by the subspace programming and linear programming.\",\"PeriodicalId\":183968,\"journal\":{\"name\":\"2013 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"volume\":\"56 3-4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE International Conference on Acoustics, Speech and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.2013.6638757\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.2013.6638757","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A K-best orthogonal matching pursuit for compressive sensing
This paper proposes an orthogonal matching pursuit (OMP-) based recovering algorithm for compressive sensing problems. This algorithm can significantly improve recovering performance while it can still maintain reasonable computational complexity. Complexity analysis and simulation results are provided for the proposed algorithm and compared with other popular recovering schemes. We observe that the proposed algorithm can significantly improve the exact recovering performance compared to the OMP scheme. Moreover, in the cases with high compressed ratio, the proposed algorithm can even outperform the benchmark performance achieved by the subspace programming and linear programming.
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