Hidden tree-like quasi-Markov model and generalized technique for a class of image processing problems

Four problems of image processing, namely, those of smoothing. texture image segmentation, matching two images of similar structure, and building the local texture orientation map, are considered jointly as problems which can be treated as those of transforming the original image into another function on the image plane. We generalized statistical image processing procedure is aimed at finding a compromise between the local image-dependent information on the values of the hidden function at each pixel and the a priori information expressed in the form of some Markov smoothness constraints. For attaining a higher computation speed, instead of a full unitary prior Markov model of the hidden field, a compromise composite model is used which consists of a set of independent identical tree-like Markov neighborhood graphs