Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN

Ryo Abiko, Masaaki Ikehara

    研究成果: Conference contribution

    11 被引用数 (Scopus)

    抄録

    The removal of mixed noise is a stiff problem since the distribution of the noise cannot be predicted accurately. The most common mixed noise is the combination of Additive White Gaussian Noise (AWGN) and Impulse Noise (IN). Many methods first attempt to remove IN but it might collapse the texture of the image. In this paper, we propose a new learning-based method using convolutional neural network (CNN) for removing mixed gaussian-impulse noise. Since our denoising network can remove various level of mixed noise, neither the preprocessing for removing IN nor noise-level estimation is necessary.

    本文言語English
    ホスト出版物のタイトル2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ1717-1721
    ページ数5
    ISBN(電子版)9781479981311
    DOI
    出版ステータスPublished - 2019 5 1
    イベント44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
    継続期間: 2019 5 122019 5 17

    出版物シリーズ

    名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    2019-May
    ISSN(印刷版)1520-6149

    Conference

    Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
    国/地域United Kingdom
    CityBrighton
    Period19/5/1219/5/17

    ASJC Scopus subject areas

    • ソフトウェア
    • 信号処理
    • 電子工学および電気工学

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