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Mixed Noise Removal by Residual Learning of Deep CNN

Kang Yang1, Jielin Jiang1,2,*, Zhaoqing Pan1,2
1 School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Country Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Jielin Jiang. Email:

Journal of New Media 2020, 2(1), 1-10. https://doi.org/10.32604/jnm.2020.09356

Received 06 December 2019; Accepted 06 December 2019; Issue published 14 August 2020

Abstract

Due to the huge difference of noise distribution, the result of a mixture of multiple noises becomes very complicated. Under normal circumstances, the most common type of mixed noise is to add impulse noise (IN) and then white Gaussian noise (AWGN). From the reduction of cascaded IN and AWGN to the latest sparse representation, a great deal of methods has been proposed to reduce this form of mixed noise. However, when the mixed noise is very strong, most methods often produce a lot of artifacts. In order to solve the above problems, we propose a method based on residual learning for the removal of AWGN-IN noise in this paper. By training, our model can obtain stable nonlinear mapping from the images with mixed noise to the clean images. After a series of experiments under different noise settings, the results show that our method is obviously better than the traditional sparse representation and patch based method. Meanwhile, the time of model training and image denoising is greatly reduce

Keywords

Mixed noise denoising; residual learning; deep convolutional neural

Cite This Article

K. Yang, J. Jiang and Z. Pan, "Mixed noise removal by residual learning of deep cnn," Journal of New Media, vol. 2, no.1, pp. 1–10, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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