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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...
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...
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