Image super-resolution via two coupled dictionaries and sparse representation

In image processing, the super-resolution (SR) technique has played an important role to perform high-resolution (HR) images from the acquired low-resolution (LR) images. In this paper, a novel technique is proposed that can generate a SR image from a single LR input image. Designed framework can be used in images of different kinds. To reconstruct a HR image, it is necessary to perform an intermediate step, which consists of an initial interpolation; next, the features are extracted from this initial image via convolution operation. Then, the principal component analysis (PCA) is used to reduce information redundancy after features extraction step. Non-overlapping blocks are extracted, and for each block, the sparse representation is performed, which it is later used to recover the HR image. Using the quality objective criteria and subjective visual perception, the proposed technique has been evaluated demonstrating their competitive performance in comparison with state-of-the-art methods.