• Laser & Optoelectronics Progress
  • Vol. 57, Issue 4, 041504 (2020)
Zhihong Xi* and Kunpeng Yuan
Author Affiliations
  • College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China
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    DOI: 10.3788/LOP57.041504 Cite this Article Set citation alerts
    Zhihong Xi, Kunpeng Yuan. Super-Resolution Image Reconstruction Based on Residual Channel Attention and Multilevel Feature Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041504 Copy Citation Text show less
    Structure of ESPCN network
    Fig. 1. Structure of ESPCN network
    Structure of VDSR network
    Fig. 2. Structure of VDSR network
    Network structure of proposed algorithm
    Fig. 3. Network structure of proposed algorithm
    Recursive unit module. (a) Recursive unit module composition; (b) residual channel attention; (c) multilevel feature fusion
    Fig. 4. Recursive unit module. (a) Recursive unit module composition; (b) residual channel attention; (c) multilevel feature fusion
    Variation of mean PSNR with the number of iterations for different layers at Set5 test set
    Fig. 5. Variation of mean PSNR with the number of iterations for different layers at Set5 test set
    Relationship between number of parameters of different network structures and mean PSNR at Set5 test set
    Fig. 6. Relationship between number of parameters of different network structures and mean PSNR at Set5 test set
    Relationship between run time of different methods and mean PSNR at Set5 test set
    Fig. 7. Relationship between run time of different methods and mean PSNR at Set5 test set
    Comparison of zebra image recovered with different algorithms
    Fig. 8. Comparison of zebra image recovered with different algorithms
    Comparison of ppt image recovered with different algorithms
    Fig. 9. Comparison of ppt image recovered with different algorithms
    Multilevelfeature fusionResidualchannel attentionPSNR
    31.61
    ×31.35
    ×31.40
    ××30.94
    Table 1. Means PSNR of different RCAF model components at Set 5 test set
    Test setScaleBicubicSRCNN[8]ESPCN[10]FSRCNN[9]VDSR[11]RCAF
    233.6836.1936.3836.4537.3437.62
    Set5330.4532.4632.7132.5933.4734.00
    428.4630.1530.2930.4230.7831.61
    230.2132.1032.2032.2132.8233.24
    Set14327.5128.9929.1229.1229.5129.87
    425.9827.2327.1727.4327.6228.11
    229.4330.8830.9331.2431.5131.81
    BSD100327.0828.0628.1628.2528.4328.72
    425.8426.6326.5926.8526.8727.18
    Table 2. Mean PSNR of different algorithms at Set5, Set14, and BSD100 test sets
    DatasetScaleBicubicSRCNN[8]ESPCN[10]FSRCNN[9]VDSR[11]RCAF
    20.9310.9550.9570.9570.9580.963
    Set530.8690.9110.9150.9120.9190.930
    40.8100.8620.8630.8660.8750.893
    20.8690.9580.9600.9630.9100.964
    Set1430.7740.8840.8870.8920.8270.897
    40.7020.8210.8230.8270.7590.842
    20.8440.8800.8830.8870.8920.897
    BSD10030.7400.7760.7810.7800.7930.797
    40.6700.6920.6940.7010.7190.720
    Table 3. Mean SSIM of different algorithms at test sets Set5, Set14, and BSD100
    Zhihong Xi, Kunpeng Yuan. Super-Resolution Image Reconstruction Based on Residual Channel Attention and Multilevel Feature Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041504
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