Restoration of severely blurred high range images using compound models

Author

Molina, R. ; Katsaggelos, K.A. ; Mateos, J. ; Abad, J.

Author_Institution

Dept. de Ciencias de la Comput. e Inteligencia Artificial, Granada Univ., Spain

Volume

1

fYear

1996

fDate

16-19 Sep 1996

Firstpage

469

Abstract

We examine the use of compound Gauss Markov random fields (CGMRF) to restore severely blurred high range images. For this deblurring problem, the convergence of the simulated annealing (SA) and iterative conditional mode (ICM) algorithms has not been established. We propose two new iterative restoration algorithms which extend the classical SA and ICM approaches. Their convergence is established and they are tested on real and synthetic images

Keywords

Gaussian processes; Markov processes; convergence of numerical methods; image restoration; iterative methods; random processes; simulated annealing; MAP estimation; algorithm convergence; compound Gauss Markov random fields; compound models; deblurring problem; image restoration; iterative conditional mode algorithm; iterative restoration algorithms; real images; severely blurred high range images; simulated annealing algorithm; synthetic images; Computational modeling; Gaussian processes; Image converters; Image restoration; Iterative algorithms; Iterative methods; Markov random fields; Pixel; Simulated annealing; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560889

Filename

560889