Convex dictionary learning for single image super-resolution

In recent years, dictionary learning approaches have been used in image super-resolution, achieving promising results. Such approaches train a dictionary from image patches and reconstruct a new patch by sparse combination of the atoms of the dictionary. Typical training methods do not constrain the dictionary atoms. In this paper, we propose a convex dictionary learning (CDL) algorithm by constraining the dictionary atoms to be formed by non-negative linear combination of the training data, which is a natural, desired property. We evaluate our approach by demonstrating its performance gain over typical approaches.