Image segmentation and restoration taking into account as much a priori information on the image as possibly available. Probabilistic graphical models (Markov random field, Bayesian networks etc.) are particularly well suited to this task and naturally combine with very efficient global optimization methods (graph cuts, belief propagation etc.).
Document Ink bleed-through removal with two hidden Markov random fields and a single observation field.
In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 32(3):431-447, 2010.
Inference and parameter estimation on hierarchical belief networks for image segmentation.
In Neurocomputing 73(4-6):563-569, 2010.
Binarization of Low Quality Text using a Markov Random Field Model.
in ICPR, 2002.