Doubly sparse structure in image super resolution

Toshiyuki Kato, Hideitsu Hino, Noboru Murata

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    There are a large number of image super resolution algorithms based on the sparse coding, and some algorithms realize multi-frame super resolution. For utilizing 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 offers improved numerical performance.

    本文言語English
    ホスト出版物のタイトル2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
    出版社IEEE Computer Society
    2016-November
    ISBN(電子版)9781509007462
    DOI
    出版ステータスPublished - 2016 11 8
    イベント26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
    継続期間: 2016 9 132016 9 16

    Other

    Other26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
    CountryItaly
    CityVietri sul Mare, Salerno
    Period16/9/1316/9/16

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Signal Processing

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