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 language | English (US) |
---|---|
Pages (from-to) | 555-571 |
Number of pages | 17 |
Journal | Pattern Recognition |
Volume | 33 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2000 |
Funding
This work has been supported by the “Comisión Nacional de Ciencia y Tecnologı́a” under contract TIC97-0989.
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