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Local Smoothing Constraint in Convolutional Neural Network for Image Denoising

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Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11632))

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Abstract

In this paper, we demonstrate that not only natural images but also their intermediate responses of convolutional neural networks (CNNs) have local smoothing priors. To imposing the local smoothing constraint, we design a local smoothing layer, which is able to suppress noises in a local receptive field. Further, we arrange the local smoothing layer in the early layers of CNNs to effectively capture context information, which is helpful to recovery image details. Experimental results validate that the proposed denoising method outperforms several state-of-the-art methods.

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Author information

Authors and Affiliations

  1. Beijing Institute of Computer Application, Beijing, China

    Yonghong Guo & Dongyang Zhao

  2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China

    Feng Jiang, Zhaojing Wen & Shaohui Liu

Authors
  1. Yonghong Guo
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  2. Feng Jiang
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  3. Dongyang Zhao
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  4. Zhaojing Wen
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  5. Shaohui Liu
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Corresponding authors

Correspondence to Yonghong Guo or Zhaojing Wen .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Guo, Y., Jiang, F., Zhao, D., Wen, Z., Liu, S. (2019). Local Smoothing Constraint in Convolutional Neural Network for Image Denoising. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_36

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  • DOI: https://doi.org/10.1007/978-3-030-24274-9_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24273-2

  • Online ISBN: 978-3-030-24274-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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