A novel Super-Resolution image reconstruction based on MRF

We address a novel method for super resolution based on Markov random field (MRF). Modeling image patches as MRF node, and we learn the parameters from training samples. Training sample set provide a candidate high-resolution interpretation for the low-resolution images. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-resolution patches for each patch of low-resolution image. In Bayesian belief propagation, we use compatibility relationships between neighboring candidate patches to select the most probable high-resolution candidate. The experimental results show that this method can obtain the better result.