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Unsupervised Bayesian convex deconvolution based on a field with an explicit partition function
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DOI：10.1109/TIP.2007.911819.png)
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摘要
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This paper proposes a non-Gaussian Markov field with a special feature: an explicit partition function. To the best of our knowledge, this is an original contribution. Moreover, the explicit expression of the partition function enables the development of an unsupervised edge-preserving convex deconvolution method. The method is fully Bayesian, and produces an estimate in the sense of the posterior mean, numerically calculated by means of a Monte-Carlo Markov chain technique. The approach is particularly effective and the computational practicability of the method is shown on a simple simulated example.
Keyword:
Bayesian statistics
convex potentials
deconvolution
hyperparameters estimation
Monte-Carlo Markov chain
partition function
regularization
unsupervised estimation
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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