A Novel Way of Incorporating Large-Scale Knowledge into MRF Prior Model

摘要

Based on Markov Random Fields (MRF) theory, Bayesian methods have been accepted as an effective solution to overcome the ill-posed problems of image restoration and reconstruction. Traditionally, the knowledge in most of prior models is from a simply weighted differences between the pixel intensities within a small local neighborhood, so it can only provide limited prior information for regularization. Exploring the ways of incorporating more large-scale knowledge into prior model, this paper proposes an effective approach to incorporate large-scale image knowledge into MRF prior model. And a novel nonlocal prior is put forward. Relevant experiments in emission tomography prove that the proposed MRF nonlocal prior is capable of imposing more effective regularization on original reconstructions.