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Improved Face Super-Resolution GenerativeAdversarial Networks
WANG Mengxue 1, Zhenxue Chen 1,Zhenxue Chen 1 *,ZHOU Xinjie 2
1.School of Control Science and Engineering, Shandong University, Jinan 250061
2.Shenzhen Research Institute of Shandong University, Shenzhen 518057
*Correspondence author
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Funding: National Natural ScienceFoundation of China （No.61203261, 61876099）, Shenzhen Science and Technology Research and DevelopmentFunds （No.JCYJ20170307093018753, JCYJ20170307093018753, JCYJ20180305164401921）
Opened online: 5 July 2019
Accepted by: none
Citation: WANG Mengxue, Zhenxue Chen,Zhenxue Chen.Improved Face Super-Resolution GenerativeAdversarial Networks[OL]. [ 5 July 2019] http://en.paper.edu.cn/en_releasepaper/content/4749261
 
 
The face super-resolution method is used for generating high-resolution images from low-resolution ones for better visualization. The Super-Resolution Generative Adversarial Network (SRGAN) can generate a single super-resolution image with realistic textures, which is a groundbreaking work. Based on SRGAN, we propose improved face super-resolution generative adversarial networks. The super-resolution image details generated by SRGAN usually have undesirable artifacts. To further improve visual quality, we delve into the key components of the SRGAN network architecture and improve each part to achieve a more powerful SRGAN. First, the SRGAN employs residual blocks as the core of the very deep generator network G. In this paper, we decide to employ Dense Convolutional Network blocks (Dense blocks), which connect each layer to every other layer in a feed-forward fashion as our very deep generator networks. Moreover, in the past few years, generative adversarial networks (GANs) have been applied to solve various problems. Despite its superior performance, however, it is difficult to train. A simple and effective regularization method called spectral normalization GAN (SNGAN) is used to solve this problem. We have experimentally confirmed that our proposed method is superior to the other existing method in training stability and visual improvements.
Keywords:Face super-resolution; GAN; spectral normalization; dense blocks
 
 
 

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