Open Access
ARTICLE
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
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.