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Image Denoising Using DnCNN: An Exploration Study

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Advances in Communication Systems and Networks

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 656))

Abstract

Image denoising is a crucial pre-processing step on images to restore the original image by suppressing the associated noise. This paper extends the performance study of the denoising convolutional neural network (DnCNN) architecture on images having the Gaussian noise. The DnCNN is an efficient deep learning model to estimate a residual image from the input image with the Gaussian noise. The underlying noise-free image can be estimated as the difference between the noisy image and the residue image. In this paper, we analyse the performance of DnCNN with data augmentation, batch normalisation and dropout. The experiments are conducted on the Berkeley natural image dataset, and quantitative and qualitative study has been performed. The comparison of the experimental results demonstrates that the DnCNN model converges at a faster rate and works well with a smaller dataset.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Calicut, India

    Vineeth Murali & P. V. Sudeep

Authors
  1. Vineeth Murali
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  2. P. V. Sudeep
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Corresponding author

Correspondence to Vineeth Murali .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, Kerala, India

    J. Jayakumari

  2. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    George K. Karagiannidis

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Maode Ma

  4. Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

    Syed Akhter Hossain

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Murali, V., Sudeep, P.V. (2020). Image Denoising Using DnCNN: An Exploration Study. In: Jayakumari, J., Karagiannidis, G., Ma, M., Hossain, S. (eds) Advances in Communication Systems and Networks . Lecture Notes in Electrical Engineering, vol 656. Springer, Singapore. https://doi.org/10.1007/978-981-15-3992-3_72

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  • DOI: https://doi.org/10.1007/978-981-15-3992-3_72

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3991-6

  • Online ISBN: 978-981-15-3992-3

  • eBook Packages: EngineeringEngineering (R0)

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