Single Image Super-Resolution Based on Local Self-Similarity

Single image super-resolution, namely increasing the resolution from only one coarse-resolution image, is a fundamental problem in computer vision. Although it has been extensively studied for decades, super-resolving a real-world image still remains challenging. In this paper, we propose a novel approach for image super-resolution by exploiting local self-similarity. First, we take advantage of this property by binding several similar patches found in a limited window into a group. Then, a novel super resolution technique is applied in the patch-based manner along with the classical reconstruction-based framework by replacing the required multiple inputs with the aforementioned group, which consists of numerous similar patches and holds the vital registration information required in the super-resolution. Experimental results demonstrate the high quality of proposed algorithm through objective DIIVINE index score as well as the subjective user study evaluation. Both ways of evaluation strongly support that the proposed single-image super-resolution algorithm is competitive and provides satisfactory results.