29 October 1993 Cluster approximations for statistical image processing
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Abstract
A disadvantage of using discrete-state Markov random field models of images is that optimal estimators for reconstruction problems require excessive and typically random amounts of computation. In one approach the key task is the computation of the conditional mean of the field given the data or equivalently the unconditional mean of the a posteriori field. In this paper we describe a hierarchy of deterministic parallelizable methods for such computations.
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Chi-hsin Wu, Peter C. Doerschuk, "Cluster approximations for statistical image processing", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162051; https://doi.org/10.1117/12.162051
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KEYWORDS
Image restoration

Image processing

Binary data

Magnetorheological finishing

Performance modeling

Radon

Computer simulations

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