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    Partially supervised neighbor embedding for example-based image super-resolution

    Zhang, K. and Gao, X. and Li, Xuelong and Tao, D. (2011) Partially supervised neighbor embedding for example-based image super-resolution. IEEE Journal of Selected Topics in Signal Processing 5 (2), pp. 230-239. ISSN 1932-4553.

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    Abstract

    Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 14:25
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7413

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