@Article{jnm.2020.09356,
AUTHOR = {Kang Yang, Jielin Jiang, Zhaoqing Pan},
TITLE = {Mixed Noise Removal by Residual Learning of Deep CNN},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {1--10},
URL = {http://www.techscience.com/JNM/v2n1/39836},
ISSN = {2579-0129},
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},
DOI = {10.32604/jnm.2020.09356}
}