Parameter estimation in bayesian super-resolution image reconstruction from low resolution rotated and translated images

Salvador Villena*, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

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

2 Scopus citations

Abstract

This paper deals with the problem of high-resolution (HR) image reconstruction, from a set of degraded, under-sampled, shifted and rotated images, utilizing the variational approximation within the Bayesian paradigm. The proposed inference procedure requires the calculation of the covariance matrix of the HR image given the LR observations and the unknown hyperparameters of the probabilistic model. Unfortunately the size and complexity of such matrix renders its calculation impossible, and we propose and compare three alternative approximations. The estimated HR images are compared with images provided by other HR reconstruction methods.

Original languageEnglish (US)
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 11th International Conference, ACIVS 2009, Proceedings
Pages188-199
Number of pages12
DOIs
StatePublished - 2009
Event11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009 - Bordeaux, France
Duration: Sep 28 2009Oct 2 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5807 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2009
CountryFrance
CityBordeaux
Period9/28/0910/2/09

Keywords

  • Bayesian paradigm
  • Covariance matrix calculation
  • High resolution images
  • Variational inference

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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