Map-MRF Super-Resolution Image Reconstruction using Maximum Pseudo-Likelihood parameter estimation

In this paper, we address the parameter estimation of a super-resolution image reconstruction approach following a maximum a posteriori probability (MAP) algorithm. The generalized isotropic multi-level logistic (GIMLL) Markov random field (MRF) model is considered for the high-resolution image characterization. In most applications, MRF model parameters are still chosen by a trial-and-error procedure through simple manual adjustments. In order to overcome this problem we propose a novel approach based on interval parameter estimation using both the maximum pseudo-likelihood (MPL) technique and an approximation of the asymptotic variance of this estimator. To evaluate the capability of the proposed estimator we used a Markov chain Monte Carlo algorithm to generate GIMLL model outcomes. The differences between the real parameters and the proposed MPL estimators are not significant. Moreover, the normalized mean square error (NMSE) of the high-resolution estimations indicate the effectiveness of our approach and the importance of an accurate estimation procedure.