Multi-frame image super resolution based on sparse coding

Toshiyuki Kato, Hideitsu Hino, Noboru Murata

    Research output: Contribution to journalArticle

    15 Citations (Scopus)

    Abstract

    An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images.

    Original languageEnglish
    Pages (from-to)64-78
    Number of pages15
    JournalNeural Networks
    Volume66
    DOIs
    Publication statusPublished - 2015 Jun 1

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    Keywords

    • Image super resolution
    • Multi-frame super-resolution
    • Sparse coding

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Cognitive Neuroscience

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