DocumentCode :
398457
Title :
Bayesian parameter estimation in image reconstruction from subsampled blurred observations
Author :
Vega, Miguel ; Mateos, Javier ; Molina, Rafael ; Katsaggelos, Aggelos K.
Author_Institution :
Dpto. de Lenguajes y Sistemas Informaticos, Granada Univ., Spain
Volume :
2
fYear :
2003
fDate :
14-17 Sept. 2003
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.
Keywords :
Bayes methods; image reconstruction; maximum likelihood estimation; parameter estimation; Bayesian parameter estimation; high-resolution image reconstruction; iterative calculation; maximum likelihood estimate; subpixel displacement error; subsampled blurred observation; synthetic image; unknown hyperparameter estimation; Bayesian methods; Computer errors; Degradation; Image reconstruction; Image resolution; Image sensors; Parameter estimation; Signal processing; Signal resolution; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-7750-8
Type :
conf
DOI :
10.1109/ICIP.2003.1246845
Filename :
1246845
Link To Document :
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