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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Image Denoising Using DnCNN: An Exploration Study

verfasst von : Vineeth Murali, P. V. Sudeep

Erschienen in: Advances in Communication Systems and Networks

Verlag: Springer Singapore

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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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Metadaten
Titel
Image Denoising Using DnCNN: An Exploration Study
verfasst von
Vineeth Murali
P. V. Sudeep
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Advances in Communication Systems and Networks

Print ISBN: 978-981-15-3991-6

Electronic ISBN: 978-981-15-3992-3

Copyright-Jahr: 2020

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