Local learning-based image super-resolution

Lu, X. and Yuan, H. and Yuan, Y. and Yan, P. and Li, L. and Li, Xuelong (2011) Local learning-based image super-resolution. In: UNSPECIFIED (ed.) 13th International Workshop on Multimedia Signal Processing. Institute of Electrical and Electronics Engineers, pp. 1-5. ISBN 9781457714320.

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Official URL: http://dx.doi.org/10.1109/MMSP.2011.6093843

Abstract

Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2]. Neighbor embedding algorithm is based on manifold assumption, which defines that the embedded neighbor patches are contained in a single manifold. While manifold assumption does not always hold. In this paper, we present a novel local learning-based image single-frame SR reconstruction algorithm with kernel ridge regression (KRR). Firstly, Gabor filter is adopted to extract texture information from low-resolution patches as the feature. Secondly, each input low-resolution feature patch utilizes K nearest neighbor algorithm to generate a local structure. Finally, KRR is employed to learn a map from input low-resolution (LR) feature patches to high-resolution (HR) feature patches in the corresponding local structure. Experimental results show the effectiveness of our method.

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	20 Jun 2013 08:47
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7500

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