New Learning-Based Super Resolution Utilizing Total Variation Regularization Method

DOI Web Site 6 References
  • Kawamoto Yuuta
    Dept. of Electrical and Electronic Engineering, Nagoya Institute of Technology
  • Suzuki Shotaro
    Dept. of Computer Science and Engineering, Nagoya Institute of Technology
  • Goto Tomio
    Dept. of Computer Science and Engineering, Nagoya Institute of Technology
  • Hirano Satoshi
    Dept. of Computer Science and Engineering, Nagoya Institute of Technology
  • Sakurai Masaru
    Dept. of Computer Science and Engineering, Nagoya Institute of Technology

Bibliographic Information

Other Title
  • Total Variation正則化手法を用いた事例学習法による超解像画像復元(コンシューマ機器および一般)
  • Total Variation正則化手法を用いた事例学習法による超解像画像復元
  • Total Variation セイソクカ シュホウ オ モチイタ ジレイ ガクシュウホウ ニ ヨル チョウカイゾウ ガゾウ フクゲン

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Abstract

Among a lot of proposals for super resolution image reconstruction, learning-based method seems to be successful approach. But, the learning-based method still has several problems. In this paper we propose a new learning-based approach for super resolution image reconstruction utilizing Total Variation (TV) regularization method.

Journal

  • ITE Technical Report

    ITE Technical Report 35.7 (0), 7-9, 2011

    The Institute of Image Information and Television Engineers

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