A single-image super-resolution method via low-rank matrix recovery and nonlinear mappings

This paper presents a novel method for single-image superresolution (SR) reconstruction using the low-rank matrix recovery and nonlinear mappings. First, the low-rank matrix recovery is utilized to learn the underlying structures of subspaces spanned by the grouped patch features. Second, the low-rank components of low-resolution (LR) and high-resolution (HR) patch features are mapped onto high-dimensional spaces by nonlinear mappings respectively. Then the mapped high-dimensional vectors are projected onto a unified space, where the two manifolds constructed by LR and HR patches respectively have similar local geometry and the SR reconstruction is performed via neighboring embedding. The experimental results validate the effectiveness of our method and suggest that the proposed method outperforms other SR algorithms qualitatively and quantitatively.