Learning local dictionaries and similarity structures for single image super-resolution

Abstract The central task of reconstruction-based single image super-resolution (SR) approaches is to design an effective prior to well pose the solution to unknown up-sampled image. In this paper, we present a novel single image SR method by learning a set of local dictionaries and non-local similar structures from the input low-resolution (LR) image itself. The local dictionaries are learned by segmenting structurally different regions into different clusters and then training an individual dictionary for each cluster. With the learned dictionaries and similar information, each HR pixel in the expected HR image is estimated as the weighted average of a non-local dictionary (NLD)-based regression which assembles the local structural regularity and the non-local similar redundancies. We further transform the proposed NLD-based regression model into a unified regularization term for a maximum a posteriori probability (MAP) based SR framework. Thorough experimental results carried out on five publicly available datasets indicate that the proposed SR method is promising in producing high-quality images with finer details and sharper edges in terms of both quantitative and perceptual quality assessments.