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Mean fields and two-dimensional Markov random fields in image analysis
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AI
DOI：10.1007/BF01234771.png)
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In this paper we compare two iterative approaches to the problem of pixel-level image restoration when the model contains unknown parameters. Pairwise interaction models are assumed to represent the local associations in the true scene. The first approach is a variation on the EM algorithm in which Mean-field approximations are used in the E-step and a variational approximation is used in the M-step. In the second approach, each iteration involves first restoring the image using the Iterated Conditional Modes (ICM) algorithm and then updating the parameter estimates by maximising the so-called pseudolikelihood. In addition, refinements are made to the Mean-field approximation, and these are also used for restoration. The methods are compared empirically using both artificial and real noise-corrupted binary scenes. Within the comparisons, the effects of using different convergence criteria for deciding when to stop the algorithms are also investigated.
Keyword:
EM algorithm
image restoration
iterated conditional modes
Markov random fields
maximum pseudolikelihood
mean-field methods
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2
论文数:
1.9K
被引数:
1.9K
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