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, not much work has been reported on the use of this model in image restoration.
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 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.
This work has been supported by the “Comisión Nacional de Ciencia y Tecnología” under contract PB93-1110.
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© 1997 Springer-Verlag Berlin Heidelberg
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Molina, R., Katsaggelos, A.K., Mateos, J., Hermoso, A. (1997). Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound gauss Markov random fields. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_76
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DOI: https://doi.org/10.1007/3-540-62909-2_76
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