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    Multi-scale dictionary for single image super-resolution

    Zhang, K. and Gao, X. and Tao, D. and Li, Xuelong (2012) Multi-scale dictionary for single image super-resolution. In: UNSPECIFIED (ed.) IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE Computer Society, pp. 1114-1121. ISBN 9781467312264.

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    Abstract

    Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 10:53
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7301

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