Tree-Structured compressive sensing with variational bayesian analysis

Lihan He, Haojun Chen, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

97 Scopus citations

Abstract

In compressive sensing (CS) the known structure in the transform coefficients may be leveraged to improve reconstruction accuracy. We here develop a hierarchical statistical model applicable to both wavelet and JPEG-based DCT bases, in which the tree structure in the sparseness pattern is exploited explicitly. The analysis is performed efficiently via variational Bayesian (VB) analysis, and comparisons are made with MCMC-based inference, and with many of the CS algorithms in the literature. Performance is assessed for both noise-free and noisy CS measurements, based on both JPEG-DCT and wavelet representations. © 2009 IEEE.
Original languageEnglish (US)
Pages (from-to)233-236
Number of pages4
JournalIEEE Signal Processing Letters
Volume17
Issue number3
DOIs
StatePublished - Nov 12 2010
Externally publishedYes

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