Laser & Optoelectronics Progress, Volume. 58, Issue 2, 0210018(2021)
Improved Super-Resolution Image Reconstruction Algorithm
Figures & Tables(16)
Fig. 6. Reconstruction effects of different algorithms. (a) Bicubic; (b) SRCNN; (c) FSRCNN; (d) our algorithm
Fig. 7. Reconstruction effect of the actual acquired image. (a) Low-resolution images; (b) our algorithm; (c) high-resolution images
Table 1. MSE of different algorithms
View tableTable 1. MSE of different algorithms
Algorithm Baby Bird Butterfly Head Woman Bicubic 27.28 35.31 247.33 34.75 93.45 SRCNN 21.97 23.78 134.33 30.86 65.77 FSRCNN 22.41 23.60 127.43 30.53 62.93 RD-SRCNN 21.09 17.69 86.44 28.79 49.95
Table 2. SSIM of different algorithms
View tableTable 2. SSIM of different algorithms
Algorithm Baby Bird Butterfly Head Woman Bicubic 0.90 0.92 0.82 0.80 0.88 SRCNN 0.91 0.94 0.87 0.81 0.91 FSRCNN 0.91 0.95 0.88 0.82 0.91 RD-SRCNN 0.92 0.96 0.92 0.83 0.93
Table 3. Reconstruction effects of different algorithms unit: dB
View tableTable 3. Reconstruction effects of different algorithms unit: dB
Algorithm Baby Bird Butterfly Head Woman Bicubic 33.77 32.65 24.19 32.72 28.42 SRCNN 34.71 34.21 26.54 33.23 29.95 FSRCNN 34.72 34.40 27.07 33.28 30.14 RD-SRCNN 34.88 35.65 28.76 33.54 31.15
Table 4. Comparison of different activation functions in the 5-layer network structure
View tableTable 4. Comparison of different activation functions in the 5-layer network structure
Evaluation indicator Activation function Baby Bird Butterfly Head Woman MSE ELU 21.31 23.12 123.51 30.29 58.82 ReLU 22.41 23.60 127.43 30.53 62.93 SSIM ELU 0.91 0.94 0.89 0.82 0.92 ReLU 0.91 0.94 0.88 0.81 0.91 PSNR /dB ELU 34.84 34.49 27.21 33.32 30.43 ReLU 34.62 34.40 27.07 33.28 30.14
Table 5. Comparison of different activation functions in the 8-layer network structure
View tableTable 5. Comparison of different activation functions in the 8-layer network structure
Evaluation indicator Activation function Baby Bird Butterfly Head Woman MSE ELU 21.23 21.18 106.74 29.27 56.26 ReLU 22.33 21.86 113.67 29.80 56.43 SSIM ELU 0.92 0.95 0.90 0.83 0.92 ReLU 0.91 0.94 0.89 0.82 0.92 PSNR /dB ELU 34.84 34.92 27.84 33.46 30.62 ReLU 34.73 34.86 27.57 33.39 30.61
Table 6. ELU activation function performance in 5-layer network structure
View tableTable 6. ELU activation function performance in 5-layer network structure
Evaluation indicator Residual structure Baby Bird Butterfly Head Woman MSE yes 21.16 18.18 87.05 29.35 50.17 no 21.31 23.12 123.57 30.29 58.82 SSIM yes 0.92 0.95 0.92 0.83 0.93 no 0.91 0.94 0.89 0.82 0.92 PSNR /dB yes 34.88 35.53 28.73 33.45 31.13 no 34.84 34.49 27.21 33.32 30.43
Table 7. ELU activation function performance in 8-layer network structure
View tableTable 7. ELU activation function performance in 8-layer network structure
Evaluation indicator Residual structure Baby Bird Butterfly Head Woman MSE yes 21.16 18.18 86.44 29.35 49.95 no 21.53 20.38 105.43 29.55 56.27 SSIM yes 0.92 0.96 0.92 0.83 0.93 no 0.91 0.95 0.90 0.82 0.92 PSNR /dB yes 34.88 35.65 28.76 33.54 31.15 no 34.79 35.03 27.9 33.42 30.62
Table 8. Performance indicators of deconvolution and non-deconvolution
View tableTable 8. Performance indicators of deconvolution and non-deconvolution
Evaluation indicator Deconvolution Baby Bird Butterfly Head Woman MSE yes 21.09 17.69 86.44 28.79 49.95 no 21.16 18.18 87.05 29.35 50.17 SSIM yes 0.92 0.96 0.92 0.83 0.93 no 0.92 0.95 0.92 0.83 0.93 PSNR /dB yes 34.88 35.65 28.76 33.54 31.15 no 34.88 35.53 28.73 33.45 31.13
Table 9. Comparison of training time
View tableTable 9. Comparison of training time
Method Times /s Method Time /s Eight layers with no residual 31.638 5- layer network 11.863 Eight layers with residual 30.071 8-layer network 8.152
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Haicheng Qu, Bowen Tang, Guisen Yuan. Improved Super-Resolution Image Reconstruction Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210018
Paper Information
Category: Image Processing
Received: May. 28, 2020
Accepted: Jul. 20, 2020
Published Online: Jan. 5, 2021
The Author Email: Qu Haicheng (quhaicheng@Intu.edu.cn), Tang Bowen (quhaicheng@Intu.edu.cn), Yuan Guisen (quhaicheng@Intu.edu.cn)
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