Image Denoising Using DnCNN: An Exploration Study

  • Vineeth MuraliEmail author
  • P. V. Sudeep
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 656)


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.


Convolutional neural networks Deep learning Denoising Gaussian noise Residual learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology CalicutCalicutIndia

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