作者
Richard G Baraniuk, Volkan Cevher, Marco F Duarte, Chinmay Hegde
发表日期
2010/3/22
期刊
IEEE Transactions on information theory
卷号
56
期号
4
页码范围
1982-2001
出版商
IEEE
简介
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis. Instead of taking periodic samples, CS measures inner products with M < N random vectors and then recovers the signal via a sparsity-seeking optimization or greedy algorithm. Standard CS dictates that robust signal recovery is possible from M = O(K log(N/K)) measurements. It is possible to substantially decrease M without sacrificing robustness by leveraging more realistic signal models that go beyond simple sparsity and compressibility by including structural dependencies between the values and locations of the signal coefficients. This paper introduces a model-based CS theory that parallels the conventional theory and provides concrete guidelines on how to create model-based recovery algorithms …
引用总数
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RG Baraniuk, V Cevher, MF Duarte, C Hegde - IEEE Transactions on information theory, 2010