Procedings of the British Machine Vision Conference 2012 2012
DOI: 10.5244/c.26.135
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Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding

Abstract: 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 … Show more

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Cited by 2,333 publications
(1,266 citation statements)
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References 14 publications
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“…Bevilacqua et al [15] presented super-resolution through neighbour embedding. In this method the generation of a high-resolution image patch does not depend on only one of the nearest neighbours in the training set.…”
Section: Related Workmentioning
confidence: 99%
“…Bevilacqua et al [15] presented super-resolution through neighbour embedding. In this method the generation of a high-resolution image patch does not depend on only one of the nearest neighbours in the training set.…”
Section: Related Workmentioning
confidence: 99%
“…Until recently, (Romano et al 2016) , (Dong et al 2016) and (Dong et al 2015) the trend of SR evolved totally towards the example-based methods. Example-based methods use a dictionary of mapping between LR and HR to infer the unknown HR details (Bevilacqua et al 2012) It exploits the self-similarity and generate patches from the input images.…”
Section: Super-resolutionmentioning
confidence: 99%
“…Machine learning algorithms contribute here by learning the construction process of the detailed sub-pixel level in the super-resolved image. Several Super-Resolution techniques gained attention mostly from computer scientists (Dong et al 2015) (Bevilacqua et al 2012) (Hong Chang et al 2004) yet to have the same from remote sensing communities. Some recent research done on remote sensing data e.g.…”
Section: Introductionmentioning
confidence: 99%
“…단일 영상 정보를 이용한 SR기법에는 대표적으로 영상의 자기유사성을 [2] 이용한 기법이 있고, 학습기반의 SR기법들 이있고 [3][4][5] 이외에도 다양한 SR기법들이 [6][7][8] [11] 와 JNB(Just Noticeable Blur) [12] [13] . [4] 및 A+ [5] 은 …”
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