2D sparse signal recovery via 2D orthogonal matching pursuit
نویسندگان
چکیده
منابع مشابه
Orthogonal Matching Pursuit for Sparse Signal Recovery
We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedy algorithm that selects at each step the column which is most correlated with the current residuals. In this paper, we present a fully data driven OMP algorithm with explicit stopping rules. It is shown t...
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Since its emergence, compressive sensing (CS) has attracted many researchers’ attention. In the CS, recovery algorithms play an important role. Basis pursuit (BP) and matching pursuit (MP) are two major classes of CS recovery algorithms. However, both BP and MP are originally designed for one-dimensional (1D) sparse signal recovery, while many practical signals are two-dimensional (2D), e.g. im...
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This article demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results for OMP, which require O(m) measurements. The new results for OMP are comparable with recent resu...
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In this paper, we propose a low-cost algorithm for recovering multitone signals from compressive measurements. We introduce a simple and efficient modification to orthogonal matching pursuit. Our approach uses a DFT basis, but refines the frequency estimate obtained at each iteration via a simple gradient descent. We find that by adapting the dictionary in this manner we can realize the benefit...
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This paper considers the exact recovery of k-sparse signals in the noiseless setting and support recovery in the noisy case when some prior information on the support of the signals is available. This prior support consists of two parts. One part is a subset of the true support and another part is outside of the true support. For k-sparse signals x with the prior support which is composed of g ...
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ژورنال
عنوان ژورنال: Science China Information Sciences
سال: 2012
ISSN: 1674-733X,1869-1919
DOI: 10.1007/s11432-012-4551-5