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• W1875923193 abstract "Compressed sensing is a developing field aiming at reconstruction of sparse signals acquired in reduced dimensions, which make the recovery process under-determined. The required solution is the one with minimum $ell_0$ norm due to sparsity, however it is not practical to solve the $ell_0$ minimization problem. Commonly used techniques include $ell_1$ minimization, such as Basis Pursuit (BP) and greedy pursuit algorithms such as Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP). This manuscript proposes a novel semi-greedy recovery approach, namely A* Orthogonal Matching Pursuit (A*OMP). A*OMP performs A* search to look for the sparsest solution on a tree whose paths grow similar to the Orthogonal Matching Pursuit (OMP) algorithm. Paths on the tree are evaluated according to a cost function, which should compensate for different path lengths. For this purpose, three different auxiliary structures are defined, including novel dynamic ones. A*OMP also incorporates pruning techniques which enable practical applications of the algorithm. Moreover, the adjustable search parameters provide means for a complexity-accuracy trade-off. We demonstrate the reconstruction ability of the proposed scheme on both synthetically generated data and images using Gaussian and Bernoulli observation matrices, where A*OMP yields less reconstruction error and higher exact recovery frequency than BP, OMP and SP. Results also indicate that novel dynamic cost functions provide improved results as compared to a conventional choice." @default.
• W1875923193 created "2016-06-24" @default.
• W1875923193 creator A5007995355 @default.
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• W1875923193 date "2012-07-01" @default.
• W1875923193 modified "2023-10-16" @default.
• W1875923193 title "A* orthogonal matching pursuit: Best-first search for compressed sensing signal recovery" @default.
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• W1875923193 doi "https://doi.org/10.1016/j.dsp.2012.03.003" @default.
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