Super-Resolution Image Reconstruction using the ICM Algorithm

Super-resolution image reconstruction is a powerful methodology for resolution enhancement from a set of blurred and noisy low-resolution images. Following a Bayesian framework, we propose a procedure for super-resolution image reconstruction based on Markov random fields (MRF), where a Potts-Strauss model is assumed for the a priori probability density function of the actual image. The first step is given by aligning all the low-resolution observations over a high-resolution grid and then improving the resolution through the iterated conditional modes (ICM) algorithm. The method was analyzed considering a number of simulated low-resolution and globally translated observations and the results demonstrate the effectiveness of the algorithm in reconstructing the desirable high-resolution image.