Employing structural and statistical information to learn dictionary(s) for single image super-resolution in sparse domain

It has been argued that structural information plays a significant role in the perceptual quality of images, but the importance of statistical information cannot be neglected. In this work, we have proposed an approach, which explores both structural and statistical information of image patches to learn multiple dictionaries for super-resolving an image in sparse domain. Structural information is estimated using dominant edge orientation, and mean value of the intensity levels of an image patch is used to represent statistical information. During reconstruction, a low resolution test patch is inspected for its structural as well as statistical information to choose a suitable dictionary. This helps in preserving the orientation of edge during super-resolution process. Results are further improved by adding an edge (magnitude of edge) preserving constraint, which maintains the edge continuity of super-resolved image with the input low resolution image. Thus, both characteristics of edge, i.e., orientation and magnitude are preserved in our proposed approach. The experimental results demonstrate the usefulness of the proposed approach in comparison to state-of-the-art approaches.