An improved algorithm is presented for super resolution based on machine learning.A training sample set is set up which contains low-resolution images and the corresponding high-resolution images.These training samples provide high-resolution image interpretation for the low-resolution images.Every image in the training set is divided into several patches and each of them is assigned one node of a Markov Random Field(MRF).The parameters of MRF are learned from these training samples and the probability dist...