Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound Gauss-Markov random fields

Rafael Molina*, Aggelos K. Katsaggelos, Javier Mateos, Aurora Hermoso, C. Andrew Segall

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Over the last few years, a growing number of researchers from varied disciplines have been utilizing Markov random fields (MRF) models for developing optimal, robust algorithms for various problems, such as texture analysis, image synthesis, classification and segmentation, surface reconstruction, integration of several low level vision modules, sensor fusion and image restoration. However, no much work has been reported on the use of Simulated Annealing (SA) and Iterative Conditional Mode (ICM) algorithms for maximum a posteriori estimation in image restoration problems when severe blurring is present. In this paper 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 SA and ICM algorithms has not been established. We propose two new iterative restoration algorithms which can be considered as extensions of the classical SA and ICM approaches and whose convergence is established. Finally, they are tested on real and synthetic images and the results compared with the restorations obtained by other iterative schemes.

Original languageEnglish (US)
Pages (from-to)555-571
Number of pages17
JournalPattern Recognition
Volume33
Issue number4
DOIs
StatePublished - Apr 2000

Keywords

  • Compound Gauss-Markov random fields
  • Image restoration
  • Iterative conditional mode
  • Simulated annealing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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