Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors

Esteban Vera*, Luis Mancera, S. Derin Babacan, Rafael Molina, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (US)
Title of host publication2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
Pages229-232
Number of pages4
DOIs
StatePublished - Dec 25 2009
Event2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 - Cardiff, United Kingdom
Duration: Aug 31 2009Sep 3 2009

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
CountryUnited Kingdom
CityCardiff
Period8/31/099/3/09

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors'. Together they form a unique fingerprint.

  • Cite this

    Vera, E., Mancera, L., Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2009). Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors. In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 (pp. 229-232). [5278598] (IEEE Workshop on Statistical Signal Processing Proceedings). https://doi.org/10.1109/SSP.2009.5278598