Example-based super-resolution via social images

A novel image patch based example-based super-resolution algorithm is proposed for benefitting from social image data. The proposed algorithm is designed based on matrix-value operator learning techniques where the image patches are understood as the matrices and the single-image super-resolution is treated as a problem of learning a matrix-value operator. Taking advantage of the matrix trick, the proposed algorithm is so fast that it could be trained on social image data. To our knowledge, the proposed algorithm is the fastest single-image super-resolution algorithm when both training and test time are considered. Experimental results have shown the efficiency and the competitive performance of the proposed algorithm to most of state-of-the-art single-image super-resolution algorithms.