Bayesian parameter estimation in image reconstruction from subsampled blurred observations

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

*Corresponding author for this work

Research output: Contribution to conferencePaper

10 Scopus citations

Abstract

In this paper we consider the estimation of the unknown hyperparameters for the problem of reconstructing a high-resolution image from multiple undersampled, shifted, blurred and degraded frames with subpixel displacement errors. We derive mathematical expressions for the iterative calculation of the maximum likelihood estimate (mle) of the unknown hyperparameters given the low resolution observed images. Finally, the proposed method is tested on a synthetic image.

Original languageEnglish (US)
Pages969-972
Number of pages4
StatePublished - Dec 17 2003
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: Sep 14 2003Sep 17 2003

Other

OtherProceedings: 2003 International Conference on Image Processing, ICIP-2003
CountrySpain
CityBarcelona
Period9/14/039/17/03

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Vega, M., Mateos, J., Molina, R., & Katsaggelos, A. K. (2003). Bayesian parameter estimation in image reconstruction from subsampled blurred observations. 969-972. Paper presented at Proceedings: 2003 International Conference on Image Processing, ICIP-2003, Barcelona, Spain.