Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images

Rafael Molina*, Miguel Vega, Javier Mateos, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

In this paper we present a super resolution Bayesian methodology for pansharpening of multispectral images. By following the hierarchical Bayesian framework, and by applying variational methods to approximate probability distributions this methodology is able to: (a) incorporate prior knowledge on the expected characteristics of the multispectral images, (b) use the sensor characteristics to model the observation process of both panchromatic and multispectral images, (c) include information on the unknown parameters in the model in the form of hyperprior distributions, and (d) estimate the parameters of the hyperprior distributions on the unknown parameters together with the unknown parameters, and the high resolution multispectral image. Using real data, the pansharpened multispectral images are compared with the images obtained by other pansharpening methods and their quality is assessed both qualitatively and quantitatively.

Original languageEnglish (US)
Pages (from-to)251-267
Number of pages17
JournalApplied and Computational Harmonic Analysis
Volume24
Issue number2
DOIs
StatePublished - Mar 2008

Keywords

  • Hierarchical Bayesian modeling
  • Multispectral images
  • Pansharpening
  • Super resolution image reconstruction
  • Variational distribution approximation

ASJC Scopus subject areas

  • Applied Mathematics

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