Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (24): 194-200.DOI: 10.3778/j.issn.1002-8331.1911-0095

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Super-Resolution of Single Image Based on Coupled Generative Adversarial Learning

ZHANG Heshu, LI Tao, SONG Gongfei   

  1. 1.School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2.Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai 200237, China
    3.Collaborative Innovation Center of Atmospheric Enviroment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • Online:2020-12-15 Published:2020-12-15



  1. 1.南京信息工程大学 自动化学院,南京 210044
    2.化工过程先进控制和优化技术教育部重点实验室,上海 200237
    3.南京信息工程大学 江苏省大气环境与装备技术协同创新中心,南京 210044


Despite the deep convolutional neural networks make breakthroughs in accuracy and speed of single image super-resolution, the central problems remain largely unsolved: the reconstruction results for traditional methods which are not obvious and too smooth. In this paper, a single image super resolution method based on coupled generative adversarial networks is proposed to solve the central problems. The deep residual network and the deep convolutional neural network are used as the generator and the discriminator. The attention augmented convolution is applied to the generator. In order to enhance the quality of the generated image and the stability of the training process, strike the right balance between the capabilities of generators and discriminators. The relative discriminator is used to calculate the loss value from adversarial neural network. Compared with the mainstream super-resolution reconstruction algorithm on the basic of Set5, Set14 and BSD100 datasets, the experimental results show that the proposed approach achieves consistently better visual quality with more sharper, realistic and high-frequency textures than prior methods, and is effective in diversified image generation applications.

Key words: super-resolution, generative adversarial network, attention augmented, deep residual network, diversity



关键词: 超分辨率, 生成对抗网络, 自注意力增强, 深度残差网络, 多样性