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… (More)
DOI: 10.1109/LSP.2013.2286417

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Cite this paper

@article{Chen2014LowRankNE, title={Low-Rank Neighbor Embedding for Single Image Super-Resolution}, author={Xiaoxuan Chen and Chun Qi}, journal={IEEE Signal Processing Letters}, year={2014}, volume={21}, pages={79-82} }