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Improved face super-resolution generative adversarial networks

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Abstract

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 proposed 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, it is difficult to train. A simple and effective regularization method called spectral normalization GAN 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.

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Acknowledgements

The authors would like to thank Karras et al. for sharing the CelebAHQ dataset and Han, Hu et al. for sharing LFW dataset. This work was supported in part by the National Natural Science Foundation of China (61876099), in part by the National Key R&D Program of China (2019YFB1311001), in part by the Scientific and Technological Development Project of Shandong Province (2019GSF111002), in part by the Shenzhen Science and Technology Research and Development Funds (JCYJ20180305164401921), in part by the Foundation of Ministry of Education Key Laboratory of System Control and Information Processing (Scip201801), in part by the Foundation of Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education (2018ICIP03), and in part by the Foundation of State Key Laboratory of Integrated Services Networks (ISN20-06).

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Mengxue Wang & Zhenxue Chen

  2. Shenzhen Research Institute of Shandong University, Shandong University, Shenzhen, 518057, China

    Zhenxue Chen

  3. Department of Electrical and Computer Engineering, University of Windsor, Windsor, N9B 3P4, Canada

    Q. M. Jonathan Wu

  4. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    Muwei Jian

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  1. Mengxue Wang
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Correspondence to Zhenxue Chen.

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Wang, M., Chen, Z., Wu, Q.M.J. et al. Improved face super-resolution generative adversarial networks. Machine Vision and Applications 31, 22 (2020). https://doi.org/10.1007/s00138-020-01073-6

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