Laser & Optoelectronics Progress, Volume. 58, Issue 8, 0810005(2021)

Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network

Tibo Zha, Lin Luo, Kai Yang*, Yu Zhang, and Jinlong Li
Author Affiliations
  • School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
  • show less
    Figures & Tables(21)
    Structure of the generator
    Network structure after removing the BN layer
    Structure of the RRDB
    Structure of the discriminator
    Schematic diagram of the training process. (a) Actual training curve; (b) ideal training curve[16]
    Training environment of the network
    Interface of the MOI test system
    PSNR of different algorithms in the Set5 test set
    SSIM of different algorithms on the Set5 test set
    PSNR of different algorithms on the Set14 test set
    SSIM of different algorithms in the Set14 test set
    Reconstruction effects of two algorithms. (a) Original image; (b) SRGAN algorithm; (c) our algorithm
    Reconstruction results of 5 different algorithms. (a) Overall original image; (b) bicubic interpolation algorithm;(c) SRCNN algorithm; (d) VDSR algorithm; (e) SRResNet algorithm; (f) our algorithm; (g) partial original image
    Railroad track image reconstructed by 5 different algorithms. (a) Overall original image; (b) bicubic interpolation algorithm; (c) SRCNN algorithm; (d) VDSR algorithm; (e) SRResNet algorithm; (f) our algorithm; (g) partial original image
    • Table 1. Evaluation standard of the image quality

      View table

      Table 1. Evaluation standard of the image quality

      Evaluation standardScore
      No change in image quality5
      Slight change in image quality can be seen4
      Slightly hinder viewing3
      Hinder viewing2
      Seriously obstructing viewing1
    • Table 2. Scoring table for subjective evaluation of image quality

      View table

      Table 2. Scoring table for subjective evaluation of image quality

      Relative measurement scaleScore
      Quality is the worst in this picture group1
      Quality is below average in this picture group2
      Quality is on average in this picture group3
      Quality is above average in this picture group4
      Quality is the best in this picture group5
    • Table 3. Test results of different algorithms on the Set5 data set

      View table

      Table 3. Test results of different algorithms on the Set5 data set

      AlgorithmSRGAN[13]OursDifference
      PSNR/dB28.7429.60↑0.86
      SSIM0.84350.8558↑0.0123
    • Table 4. Test results of different algorithms on the Set14 data set

      View table

      Table 4. Test results of different algorithms on the Set14 data set

      AlgorithmSRGAN[13]OursDifference
      PSNR/dB25.7526.44↑0.69
      SSIM0.73700.7460↑0.0090
    • Table 5. Test results of different algorithms on the BSD100 data set

      View table

      Table 5. Test results of different algorithms on the BSD100 data set

      AlgorithmSRGAN[13]OursDifference
      PSNR/dB24.6525.55↑0.90
      SSIM0.65020.6549↑0.0047
    • Table 6. Influence of BN layer on algorithm performance

      View table

      Table 6. Influence of BN layer on algorithm performance

      Data setSRGAN(with BN)Ours(with BN)SRGAN(without BN)Ours(without BN)
      PSNR /dBSSIMTime /sPSNR /dBSSIMTime /sPSNR /dBSSIMTime /sPSNR /dBSSIMTime /s
      Set528.690.84150.2129.020.84890.2029.250.84830.2029.600.85470.18
      Set1424.960.71870.4326.100.72330.3925.670.72030.4226.410.73980.38
      BSD10024.010.64880.4525.150.65110.4325.100.65030.4425.520.65480.41
    • Table 7. Performance of different algorithms under three data sets

      View table

      Table 7. Performance of different algorithms under three data sets

      DatasetAlgorithmBicubicSRCNN[2]VDSR[5]SRResNet[13]Ours
      PSNR /dB28.4330.1431.3531.9229.60
      Set5SSIM0.82110.86470.88380.89980.8558
      MOI1.442.43.183.284.66
      PSNR /dB25.9927.1828.0128.3926.44
      Set14SSIM0.74860.78610.76740.81160.746
      MOI1.422.433.183.594.38
      PSNR /dB25.9626.927.2927.5225.55
      BSD100SSIM0.66750.71010.72510.76030.6549
      MOI1.362.453.23.574.42
    Tools

    Get Citation

    Copy Citation Text

    Tibo Zha, Lin Luo, Kai Yang, Yu Zhang, Jinlong Li. Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810005

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Jul. 28, 2020

    Accepted: Sep. 10, 2020

    Published Online: Apr. 12, 2021

    The Author Email: Yang Kai (yangkai_swjtu@163.com)

    DOI:10.3788/LOP202158.0810005

    Topics