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Improved Blind Image Denoising with DnCNN

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Denoising convolutional neural networks (DnCNN) has been proposed recently for image denoising for additive white Gaussian noise with both blind and non-blind versions. For blind DnCNN, the networks are trained with noise levels from 0 to 55, which is not perfect for other noise levels. In this paper, we train the DnCNN with three noise ranges [0, 40], [40, 80], and [80, 120] separately to obtain three network models so that better denoising results can be achieved. The training of our new models can be done in parallel by taking advantages of GPUs. We choose the suitable network model according to the estimated noise level from the noisy images. Experimental results demonstrate that our proposed method outperforms the standard DnCNN for image denoising for almost all testing cases with all six testing images.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Guang Yi Chen & Adam Krzyzak

  2. Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada

    Wenfang Xie

Authors
  1. Guang Yi Chen
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  2. Wenfang Xie
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  3. Adam Krzyzak
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Corresponding author

Correspondence to Guang Yi Chen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Chen, G.Y., Xie, W., Krzyzak, A. (2023). Improved Blind Image Denoising with DnCNN. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_21

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  • DOI: https://doi.org/10.1007/978-981-99-4742-3_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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