relation: http://eprints.imtlucca.it/2412/
title: Low-complexity single-image super-resolution based on nonnegative neighbor embedding
creator: Bevilacqua, Marco
creator: Roumy, Aline
creator: Guillemot, Christine
creator: Alberi-Morel, Marie Line
subject: QA75 Electronic computers. Computer science
description: This paper describes a single-image super-resolution (SR) algorithm based on nonnegative neighbor embedding. It belongs to the family of single-image example-based  SR algorithms, since it uses a dictionary of low resolution (LR) and high resolution (HR) trained patch pairs to infer the unknown HR details. Each LR feature vector in the input  image is expressed as the weighted combination of its K nearest neighbors in the dictionary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. Three key aspects are introduced in order to build a low-complexity competitive algorithm: (i) a compact but efficient representation of the  patches (feature representation) (ii) an accurate estimation of the patches by their nearest neighbors (weight computation) (iii) a compact and already built (therefore external) dictionary, which allows a one-step upscaling. The neighbor embedding SR algorithm so designed is shown to give good visual results, comparable to other state-of-the-art methods, while presenting an appreciable reduction of the computational time.
publisher: BMVA Press
date: 2012
type: Book Section
type: PeerReviewed
format: application/pdf
language: en
identifier: http://eprints.imtlucca.it/2412/1/Bevilacqua_2012.pdf
identifier:   Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line  Low-complexity single-image super-resolution based on nonnegative neighbor embedding.   In:  Proceedings of the 23rd British Machine Vision Conference (BMVC).   BMVA Press, 135.1-135.10.  ISBN 1-901725-46-4      (2012)  
relation: http://dx.doi.org/10.5244/C.26.135
relation: 10.5244/C.26.135