Fast image super-resolution via selective manifold learning of high-resolution patches
This paper considers the problem of single image super-resolution (SR). Previous example-based SR approaches mainly focus on analyzing the co-occurrence properties of low resolution (LR) and high resolution (HR) patches via dictionary learning. In our recent work [1], a novel approach (SR via sparse subspace clustering-based linear approximation of manifold or SLAM) has been proposed. In this paper, we further improve the SLAM method by considering and analyzing each tangent subspace as one point in a Grassmann manifold to select an optimal subset of tangent spaces. Furthermore, the optimal subset is clustered hierarchically, which helps in reducing the proposed algorithm's complexity significantly while still preserving the quality of the reconstructed HR image.