High-Frequency Restoration Using Deep Belief Nets for Super-resolution

Super-resolution technology, which restores high-frequency information given a low-resolved image, has attracted much attention recent years. Various super-resolution algorithms were proposed so far: example-based approach, sparse-coding-based, GMM (Gaussian Mixture Model), BPLP (Back Projection for Lost Pixels), and so on. Most of these statistical approaches rely on the training (or just preparing) of the correspondence relationships between low-resolved/high-resolved images. In this paper, we propose a novel super-resolution method that is based on a statistical model but does not require any pairs of low and high-resolved images in the database. In our approach, Deep Belief Bets are used to restore high-frequency information from a low-resolved image. The idea is that only using high-resolved images, the trained networks seek the high-order dependencies among the observed nodes (each spatial frequency: e.g., high and low frequencies). Experimental results show the high performance of our proposed method.