Abstract
We consider sampling methods for multidimensional random fields. We study the convergence of a sampler process constructed by the methods stated in Introduction. We can apply convergence theorems to the restoration of degraded images in image processing.
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References
J. Besag, Spacial interaction and the statistical analysis of lattice systems. J. Royal Stat. Soc., B34 (1972), 75–83.
G. C. Cross and A. K. Jain, Markov random field texture models. IEEE Trans. PAMI,5 (1983), 25–39.
S. Geman and D. Geman, Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. PAMI,6 (1984), 721–741.
S. Kirkpatrick, C. D. Gelatt and M. P. Vecchi, Optimization by simulated annealing. Science,220 (1983), 671–680.
N. Metropolis et al., Equation of state calculations by fast computing mechanics. J. Phys. Chem.,21 (1953), 1087–1092.
F. Spitzer, Markov random fields and Gibbs ensembles. Amer. Math. Monthly,78 (1971), 142–154.
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These results are based on research done while the author was visiting the Department of Statistics, University of North Carolina at Chapel Hill as an overseas researcher of the Japanese Ministry of Education. This research was partially supported by ONR Contracts N00014-81-K-0373 and N00014-84-C-0212.
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Yanagi, K. Convergence theorems of sampler processes with applications to image processing. Japan J. Appl. Math. 3, 381–393 (1986). https://doi.org/10.1007/BF03167109
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DOI: https://doi.org/10.1007/BF03167109