Low-Rank Neighbor Embedding for Single Image Super-Resolution

Abstract

This letter proposes a novel single image super-resolution (SR) method based on the low-rank matrix recovery (LRMR) and neighbor embedding (NE). LRMR is used to explore the underlying structures of subspaces spanned by similar patches. Specifically, the training patches are first divided into groups. Then the LRMR technique is utilized to learn the latent structure of each group. The NE algorithm is performed on the learnt low-rank components of HR and LR patches to produce SR results. Experimental results suggest that our approach can reconstruct high quality images both quantitatively and perceptually.