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Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery
July 1993 (vol. 15 no. 7)
pp. 748-752

Bayesian methods for recovering a 2-D surface are discussed. It is assumed that there is a textural image that can be modeled by a Markov random field and that the original surface is composed of different surfaces, each of which is associated with one textural state. Both parametric and nonparametric methods are used to enforce smoothness of these surfaces. Iterative procedures are examined for simultaneous restoration of the textural image and estimation of underlying parameters. From the estimated textural image and the estimated parameters, an estimate for the original surface is obtained. Two illustrative examples are presented.

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Index Terms:
Bayesian image restoration; image processing; 2D surface recovery; parameter estimation; parametric methods; edge-preserving surface recovery; textural image; Markov random field; nonparametric methods; Bayes methods; image reconstruction; image texture; iterative methods; Markov processes; parameter estimation
Citation:
W. Qian, D.M. Titterington, "Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 7, pp. 748-752, July 1993, doi:10.1109/34.221174
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