Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors

ESTEBAN MAURICIO VERA ROJAS, Luis Mancera, S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

In this paper, we propose a novel algorithm for image reconstruction from compressive measurements of wavelet coefficients. By incorporating independent Laplace priors on separate wavelet sub-bands, the inhomogeneity of wavelet coefficient distributions and therefore the structural sparsity within images are modeled effectively. We model the problem by adopting a Bayesian formulation, and develop a fast greedy reconstruction algorithm. Experimental results demonstrate that the reconstruction performance of the proposed algorithm is competitive with state-of-the-art methods while outperforming them in terms of running times.

Original languageEnglish
Title of host publication2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages229-232
Number of pages4
DOIs
StatePublished - 25 Dec 2009
Event2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
Duration: 31 Aug 20093 Sep 2009

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Conference

Conference2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
CountryUnited Kingdom
CityCardiff
Period31/08/093/09/09

Keywords

  • Bayesian methods
  • Compressive sensing
  • Signal reconstruction
  • Wavelet transforms

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