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Exploiting structure in wavelet-based Bayesian compressive sensing (2009)
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BibTeX
@MISC{He09exploitingstructure,
author = {Lihan He and Lawrence Carin},
title = {Exploiting structure in wavelet-based Bayesian compressive sensing },
year = {2009}
}
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Abstract
Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the proposed model, and therefore this framework goes beyond simply assuming that the data are compressible in a wavelet basis. The structure exploited within the wavelet coefficients is consistent with that used in waveletbased compression algorithms. A hierarchical Bayesian model is constituted, with efficient inference via Markov chain Monte Carlo (MCMC) sampling. The algorithm is fully developed and demonstrated using several natural images, with performance comparisons to many state-of-the-art compressive-sensing inversion algorithms.
Keyphrases
wavelet-based bayesian compressive sensing wavelet basis wavelet coefficient several natural image efficient inference markov chain monte carlo statistical structure bayesian compressive sensing waveletbased compression algorithm hierarchical bayesian model performance comparison many state-of-the-art compressive-sensing inversion algorithm
