Image interpolation based on weighting function of Gaussian

Takuro Yamaguchi, Masaaki Ikehara, Yasuhiro Nakajima

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

    1 Citation (Scopus)

    Abstract

    In this paper, we propose a new image interpolation method based on a 2-D piecewise stationary autoregressive (PAR) model. SAI, which defined PAR model, is a state-of-the-art method in image interpolation. It produces good quality restored images but has high calculation cost because of solving multiple least-square problems. Our method utilizes Gaussian function in estimating parameters instead of solving least-square problems and reduces the calculation cost. Moreover, parameters are estimated at each pixel, while they are estimated in each local window in SAI. By these improvements, the proposed method has equivalent quality to SAI with low calculation cost.

    Original languageEnglish
    Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
    PublisherIEEE Computer Society
    Pages1193-1197
    Number of pages5
    Volume2016-February
    ISBN (Print)9781467385763
    DOIs
    Publication statusPublished - 2016 Feb 26
    Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
    Duration: 2015 Nov 82015 Nov 11

    Other

    Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
    CountryUnited States
    CityPacific Grove
    Period15/11/815/11/11

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Signal Processing

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  • Cite this

    Yamaguchi, T., Ikehara, M., & Nakajima, Y. (2016). Image interpolation based on weighting function of Gaussian. In Conference Record - Asilomar Conference on Signals, Systems and Computers (Vol. 2016-February, pp. 1193-1197). [7421329] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421329