Parameter estimation in bayesian high-resolution image reconstruction with multisensors

Rafael Molina*, Miguel Vega, Javier Abad, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticle

72 Scopus citations

Abstract

In this paper, we consider the estimation of the unknown parameters for the problem of reconstructing a high-resolution image from multiple undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the iterative calculation of the maximum likelihood estimate of the unknown parameters given the low resolution observed images. These iterative procedures require the manipulation of block-semi circulant (BSC) matrices, that is block matrices with circulant blocks. We show how these BSC matrices can be easily manipulated in order to calculate the unknown parameters. Finally the proposed method is tested on real and synthetic images.

Original languageEnglish (US)
Pages (from-to)1655-1667
Number of pages13
JournalIEEE Transactions on Image Processing
Volume12
Issue number12
DOIs
StatePublished - Dec 1 2003

Keywords

  • Bayesian methods
  • High-resolution image reconstruction
  • Parameter estimation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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