Subspace weighted ℓ2,1 minimization for sparse signal recovery
نویسندگان
چکیده
منابع مشابه
Subspace weighted l2, 1 minimization for sparse signal recovery
In this article, we propose a weighted l2,1 minimization algorithm for jointly-sparse signal recovery problem. The proposed algorithm exploits the relationship between the noise subspace and the overcomplete basis matrix for designing weights, i.e., large weights are appointed to the entries, whose indices are more likely to be outside of the row support of the jointly sparse signals, so that t...
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Numerical experiments in literature on compressed sensing have indicated that the reweighted l1 minimization performs exceptionally well in recovering sparse signal. In this paper, we develop exact recovery conditions and algorithm for sparse signal via weighted l1 minimization from the insight of the classical NSP (null space property) and RIC (restricted isometry constant) bound. We first int...
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Weighted l1-minimization has been studied as a technique for the reconstruction of a sparse signal from compressively sampled measurements when prior information about the signal, in the form of a support estimate, is available. In this work, we study the recovery conditions and the associated recovery guarantees of weighted l1-minimization when arbitrarily many distinct weights are permitted. ...
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ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2012
ISSN: 1687-6180
DOI: 10.1186/1687-6180-2012-98