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    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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    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.


    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


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