Choosing the Parameter of Image Restoration Filters by Modified Subspace Information Criterion

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Author(s)

    • TANAKA Akira
    • Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University
    • IMAI Hideyuki
    • Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University
    • MIYAKOSHI Masaaki
    • Division of Systems and Information Engineering, Graduate School of Engineering, Hokkaido University

Abstract

Practical image restoration filters usually include a parameter that controls regularizability, trade-off between fidelity of a restored image and smoothness of it, and so on. Many criteria for choosing such a parameter have been proposed. However, the relation between these criteria and the squared error of a restored image, which is usually used to evaluate the restoration performance, has not been theoretically substantiated. Sugiyama and Ogawa proposed the subspace information criterion (SIC) for model selection of supervised learning problems and showed that the SIC is an unbiased estimator of the expected squared error between the unknown model function and an estimated one. They also applied it to restoration of images. However, we need an unbiased estimator of the unknown original image to construct the criterion, so it can not be used for general situations. In this paper, we present a modified version of the SIC as a new criterion for choosing a parameter of image restoration filters. Some numerical examples are also shown to verify the efficacy of the proposed criterion.

Journal

  • IEICE Transactions on Fundamentals

    IEICE Transactions on Fundamentals 85(5), 1088-1095, 2002-05-01

    The Institute of Electronics, Information and Communication Engineers

References:  9

Cited by:  4

Codes

  • NII Article ID (NAID)
    110003209132
  • NII NACSIS-CAT ID (NCID)
    AA10826239
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    09168508
  • Data Source
    CJP  CJPref  NII-ELS 
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