Multiscale Markov random fields and constrained relaxation in low level image analysis
The authors investigate a new approach to multigrid image analysis based on Markov random field (MRF) models. The multigrid algorithms under consideration are based on constrained optimization schemes. The global optimization problem associated with MRF modeling is solved sequentially over particular subsets of the original configuration space. Those subsets consist of constrained configurations describing the desired resulting field at different scales. The constrained optimization can be implemented via a coarse-to-fine multigrid algorithm defined on a sequence of consistent multiscale MRF models. The proposed multiscale paradigm yields fast convergence toward high-quality estimates when compared to standard monoresolution or multigrid relaxation schemes.<<ETX>>