I am reading the seminal paper 



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> Stuart Geman and Donald Geman, *Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images*, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, no. 6, pp. 721-741, Nov. 1984. doi: [10.1109/TPAMI.1984.4767596](https://doi.org/10.1109/TPAMI.1984.4767596) ([free pdf](http://image.diku.dk/imagecanon/material/GemanPAMI84.pdf))
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on image restoration. Is there a more recent account (paper or lecture notes) with proper generalizations and incorporating the recent progress of Bayesian methods?



Updating Geman and Geman (1984) on image restoration
Given that the paper has accumulated a stunning **21.850** citations at Google scholar to this date and 487 are from 2018 the work is clearly influential (zbMATH lists 913 citations and 17 from 2018, MATHSCINET does not have it, though). It is not straightforward to answer your question "Is there a more recent account (paper or lecture notes) with proper generalizations and incorporating the recent progress of Bayesian methods?" since the paper contributed several things and the thousands of citations cite it for very different reasons (e.g. many references are just given for the highly useful block Gibbs sampling).


To give just one concrete pointer: You may want to have a look at



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> Wainwright, Martin J., and Michael I. Jordan. "Graphical models, exponential families, and variational inference." Foundations and Trends® in Machine Learning 1.1–2 (2008): 1-305.
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