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Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

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Abstract

Real-time video deblurring still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost. To improve the network efficiency, we adopt residual dense blocks into RNN cells, so as to efficiently extract the spatial features of the current frame. Furthermore, a global spatio-temporal attention module is proposed to fuse the effective hierarchical features from past and future frames to help better deblur the current frame. For evaluation, we also collect a novel dataset with paired blurry/sharp video clips by using a co-axis beam splitter system. Through experiments on synthetic and realistic datasets, we show that our proposed method can achieve better deblurring performance both quantitatively and qualitatively with less computational cost against state-of-the-art video deblurring methods.

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Author information

Authors and Affiliations

  1. The University of Tokyo, Tokyo, 113-8656, Japan

    Zhihang Zhong

  2. Tokyo Research Center, Huawei, Tokyo, Japan

    Ye Gao & Bo Zheng

  3. National Institute of Informatics, Tokyo, 101-8430, Japan

    Yinqiang Zheng

Authors
  1. Zhihang Zhong
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  2. Ye Gao
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  3. Yinqiang Zheng
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  4. Bo Zheng
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Corresponding author

Correspondence to Yinqiang Zheng .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Zhong, Z., Gao, Y., Zheng, Y., Zheng, B. (2020). Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_12

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  • Online ISBN: 978-3-030-58539-6

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