IEEE Trans Image Process - Adaptive compressed sensing recovery utilizing the property of signal's autocorrelations.

Tópicos

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Resumo

Perfect compressed sensing (CS) recovery can be achieved when a certain basis space is found to sparsely represent the original signal. However, due to the diversity of the signals, there does not exist a universal predetermined basis space that can sparsely represent all kinds of signals, which results in an unsatisfying performance. To improve the accuracy of recovered signal, this paper proposes an adaptive basis CS reconstruction algorithm by minimizing the rank of an accumulated matrix (MRAM), whose eigenvectors approximate the optimal basis sparsely representing the original signal. The accumulated matrix is constructed to efficiently exploit the second-order statistical property of the signal's autocorrelations. Based on the theory of matrix completion, MRAM reconstructs the original signal from its random projections under the observation that the constructed accumulated matrix is of low rank for most natural signals such as periodic signals and those coming from an autoregressive stationary process. Experimental results show that the proposed MRAM efficiently improves the reconstruction quality compared with the existing algorithms.

Resumo Limpo

perfect compress sens cs recoveri can achiev certain basi space found spars repres origin signal howev due divers signal exist univers predetermin basi space can spars repres kind signal result unsatisfi perform improv accuraci recov signal paper propos adapt basi cs reconstruct algorithm minim rank accumul matrix mram whose eigenvector approxim optim basi spars repres origin signal accumul matrix construct effici exploit secondord statist properti signal autocorrel base theori matrix complet mram reconstruct origin signal random project observ construct accumul matrix low rank natur signal period signal come autoregress stationari process experiment result show propos mram effici improv reconstruct qualiti compar exist algorithm

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