@Article{jihpp.2020.010453,
AUTHOR = {Peizhu Gong, Jin Liu, Shiqi Lv},
TITLE = {Image Denoising with GAN Based Model},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {155--163},
URL = {http://www.techscience.com/jihpp/v2n4/41147},
ISSN = {2637-4226},
ABSTRACT = {Image denoising is often used as a preprocessing step in computer 
vision tasks, which can help improve the accuracy of image processing models. 
Due to the imperfection of imaging systems, transmission media and recording 
equipment, digital images are often contaminated with various noises during 
their formation, which troubles the visual effects and even hinders people’s 
normal recognition. The pollution of noise directly affects the processing of 
image edge detection, feature extraction, pattern recognition, etc., making it 
difficult for people to break through the bottleneck by modifying the model.
Many traditional filtering methods have shown poor performance since they do 
not have optimal expression and adaptation for specific images. Meanwhile, deep 
learning technology opens up new possibilities for image denoising. In this paper, 
we propose a novel neural network which is based on generative adversarial 
networks for image denoising. Inspired by U-net, our method employs a novel 
symmetrical encoder-decoder based generator network. The encoder adopts 
convolutional neural networks to extract features, while the decoder outputs the 
noise in the images by deconvolutional neural networks. Specially, shortcuts are 
added between designated layers, which can preserve image texture details and 
prevent gradient explosions. Besides, in order to improve the training stability of 
the model, we add Wasserstein distance in loss function as an optimization. We 
use the peak signal-to-noise ratio (PSNR) to evaluate our model and we can 
prove the effectiveness of it with experimental results. When compared to the 
state-of-the-art approaches, our method presents competitive performance.},
DOI = {10.32604/jihpp.2020.010453}
}