Super-Resolution Image Reconstruction Based on K-Means-Markov Network
YanJie Ma, Hua Zhang, Yanbing Xue, Simiao Zhang · 2009
We address a learning-based method for super resolution. Training sample set provide a candidate high resolution interpretation for the low-resolution images. Modeling image patches as Markov network node, and we learn the parameters of the network from training set,compute probability distribution by K-means algorithm. 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 relationship between neighboring candidate patches to select the most probable high-resolution candidate. The experimental results show that this method can obtain better result.