Double sparsity for multi-frame super resolution

Toshiyuki Kato, Hideitsu Hino, Noboru Murata

    研究成果: Article査読

    9 被引用数 (Scopus)

    抄録

    A number of image super resolution algorithms based on the sparse coding have successfully implemented multi-frame super resolution in recent years. In order to utilize multiple low-resolution observations, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The proposed formulation not only has a mathematically interesting structure, called the double sparsity, but also yields comparable or improved numerical performance to conventional methods.

    本文言語English
    ページ(範囲)115-126
    ページ数12
    ジャーナルNeurocomputing
    240
    DOI
    出版ステータスPublished - 2017 5 31

    ASJC Scopus subject areas

    • Computer Science Applications
    • Cognitive Neuroscience
    • Artificial Intelligence

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