Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2010012(2021)

Attention Fusion Generative Adversarial Network for Single-Image Super-Resolution Reconstruction

Yanfei Peng**, Pingjia Zhang*, Yi Gao, and Lingling Zi
Author Affiliations
  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
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    Figures & Tables(19)
    Generator network structure
    Discriminator network structure
    Use of channel and spatial attention modules
    Reconstruction effects with different values of ε coefficient
    Comparison of residual blocks. (a)SRGAN; (b) proposed model
    Variation curve of generator function loss value
    Variation curve of discriminant function loss value
    Partial enlarged comparison diagrams of the “baby” reconstruction effect of five algorithms in Set5 test set
    Partial enlarged comparison diagrams of the “butterfly” reconstruction effect of five algorithms in Set5 test set
    Partial enlarged comparison diagrams of the “pepper” reconstruction effect of five algorithms in Set14 test set
    Partial enlarged comparison diagrams of the “fish” reconstruction effect of five algorithms in BSDS100 test set
    Partial enlarged comparison diagrams of the “room” reconstruction effect of five algorithms in Urban100 test set
    Partial enlarged comparison diagrams of the “baby” reconstruction effect in ablation experiment in Set5 test set
    Partial enlarged comparison diagrams of the “butterfly” reconstruction effect in ablation experiment in Set5 test set
    Partial enlarged comparison diagrams of the “lenna” reconstruction effect in ablation experiment in Set14 test set
    • Table 1. Comparison of PSNR values of various super-resolution reconstruction methods

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      Table 1. Comparison of PSNR values of various super-resolution reconstruction methods

      Test setScaleBicubicESPCNSRGANESRGANProposed
      Set526.69227.59426.62828.54329.510
      Set1424.56525.18624.56824.53226.443
      Urban10021.70622.30022.11322.79223.917
      BSDS10024.64125.04324.47225.32225.884
    • Table 2. Comparison of SSIM values of various super-resolution reconstruction methods

      View table

      Table 2. Comparison of SSIM values of various super-resolution reconstruction methods

      Test setScaleBicubicESPCNSRGANESRGANProposed
      Set50.77300.78950.80230.81450.8517
      Set140.67320.69830.70190.67110.7377
      Urban1000.63170.65950.67740.70500.7415
      BSDS1000.64010.67020.67130.65140.7002
    • Table 3. PSNR values of models with different module combinations on four test sets

      View table

      Table 3. PSNR values of models with different module combinations on four test sets

      MethodSet5Set14Urban100BSDS100
      SRGAN26.62824.56822.11324.472
      SRGAN-BN27.86325.26922.74325.228
      SRGAN+CA&SA27.86525.37522.69325.198
      SRGAN+Charbonnier28.15325.72422.91925.404
      SRGAN+Charbonnier+CA&SA-BN29.51026.44323.91725.884
    • Table 4. SSIM values of models with different module combinations on four test sets

      View table

      Table 4. SSIM values of models with different module combinations on four test sets

      MethodSet5Set14UrbanBSDS100
      SRGAN0.80230.70190.67740.6713
      SRGAN-BN0.80580.70790.68200.6728
      SRGAN+CA&SA0.80570.70680.67920.6754
      SRGAN+Charbonnier0.82030.71800.69390.6822
      SRGAN+Charbonnier+A&SA-BN0.85170.73770.74150.7002
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    Yanfei Peng, Pingjia Zhang, Yi Gao, Lingling Zi. Attention Fusion Generative Adversarial Network for Single-Image Super-Resolution Reconstruction[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010012

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    Paper Information

    Category: Image Processing

    Received: Nov. 25, 2020

    Accepted: Jan. 6, 2021

    Published Online: Oct. 13, 2021

    The Author Email: Peng Yanfei (pengyf75@126.com), Zhang Pingjia (1308192862@qq.com)

    DOI:10.3788/LOP202158.2010012

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