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Super-Resolution Imaging Using Convolutional Neural Networks

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

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Abstract

Convolutional neural networks (CNN) have been applied to many classic problems in computer vision. This paper utilized CNNs to reconstruct super-resolution images from low-resolution images. To improve the performance of our model, four optimizations were added in the training process. After comparing the models with these four optimizations, Adam and RMSProp were found to achieve the optimal performance in peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Considering both reconstruction accuracy and training speed, simulation results suggest that RMSProp optimization in the most scenarios.

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Author information

Authors and Affiliations

  1. College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Yingyi Sun, Wenhua Xu, Jie Zhang, Jian Xiong & Guan Gui

Authors
  1. Yingyi Sun
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  2. Wenhua Xu
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  3. Jie Zhang
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  4. Jian Xiong
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  5. Guan Gui
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Corresponding author

Correspondence to Yingyi Sun .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Sun, Y., Xu, W., Zhang, J., Xiong, J., Gui, G. (2020). Super-Resolution Imaging Using Convolutional Neural Networks. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_8

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

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