Tree-Structured compressive sensing with variational bayesian analysis

Lihan He; Haojun Chen; Lawrence Carin

doi:10.1109/LSP.2009.2037532

Tree-Structured compressive sensing with variational bayesian analysis

Lihan He, Haojun Chen, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

114 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 language	English (US)
Pages (from-to)	233-236
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	17
Issue number	3
DOIs	https://doi.org/10.1109/LSP.2009.2037532
State	Published - Nov 12 2010
Externally published	Yes

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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. {\textcopyright} 2009 IEEE.",

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AB - 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.

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ER -

Tree-Structured compressive sensing with variational bayesian analysis

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