Two--level MRF Models for Image Restoration and Segmentation
Mariano Rivera, James C. Gee · 2004
We present a new general Bayesian formulation for simultaneously restoring and segmenting piecewise smooth images. This implies estimation of the associated parameters of the classes within an image, the class label for each image pixel and the number of classes. The intensity image is modelled by parametric models based on regularized networks. The method fits the regions (or classes) with complex spatial intensity distributions with an identifiable group of simple models. Prior information is introduced in form of a two-level Markov random field (MRF). The low--level MRF models the information required to recover piecewise restorations, while the high-level MRF constraints the segmentation. The high--level MRF supports a merging process of simple intensity models into classes.