Doubly sparse structure in image super resolution

Toshiyuki Kato, Hideitsu Hino, Noboru Murata

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publication2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings
    PublisherIEEE Computer Society
    Volume2016-November
    ISBN (Electronic)9781509007462
    DOIs
    Publication statusPublished - 2016 Nov 8
    Event26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy
    Duration: 2016 Sep 132016 Sep 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

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    Keywords

    • Double Sparsity
    • Image Super Resolution
    • Sparse Coding

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Signal Processing

    Cite this

    Kato, T., Hino, H., & Murata, N. (2016). Doubly sparse structure in image super resolution. In 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings (Vol. 2016-November). [7738902] IEEE Computer Society. https://doi.org/10.1109/MLSP.2016.7738902