Bayesian reconstruction of hyperspectral images by using compressed sensing measurements and a local structured prior

Yuri Mejia, Henry Arguello, Facundo Costa, Jean-Yves Tourneret, Hadj Batatia

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

1 Citation (Scopus)

Abstract

This paper introduces a hierarchical Bayesian model for the reconstruction of hyperspectral images using compressed sensing measurements. This model exploits known properties of natural images, promoting the recovered image to be sparse on a selected basis and smooth in the image domain. The posterior distribution of this model is too complex to derive closed form expressions for the estimators of its parameters. Therefore, an MCMC method is investigated to sample this posterior distribution. The resulting samples are used to estimate the unknown model parameters and hyperparameters in an unsupervised framework. The results obtained on real data illustrate the improvement in reconstruction quality when compared to some existing techniques.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages3116-3120
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Abbreviated titleICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Bayesian reconstruction
  • compressive sampling
  • Spectral imaging

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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