Laser & Optoelectronics Progress, Volume. 55, Issue 12, 121001(2018)
Single Image Super-Resolution Based on Convolutional Neural Network
Figures & Tables(13)
Fig. 4. Graph of train loss in the proposed method with the increase of iterations in the training process
Fig. 5. Comparison of the reconstruction of the baby_GT in Set 5. (a) Original image; (b) BI/33.91 dB; (c) ScSR/34.29 dB; (d) SRCNN/34.83 dB; (e) SRCNN-Ex/34.91dB; (f) proposed method/35.04 dB
Fig. 6. Comparison of the reconstruction of the butterfly_GT in Set 5. (a) Original image; (b) BI/24.04 dB; (c) ScSR/25.58 dB; (d) SRCNN/25.00 dB; (e) SRCNN-Ex/25.58 dB; (f) proposed method/27.91 dB
Fig. 7. Comparison of the reconstruction of the lenna in Set 14. (a) Original image; (b) BI/31.68 dB; (c) ScSR/32.64 dB; (d) SRCNN/32.53 dB; (e) SRCNN-Ex/32.78 dB; (f) proposed method/33.57 dB
Fig. 8. Comparison of the reconstruction of the pepper in Set 14. (a) Original image; (b) BI/32.38 dB; (c) ScSR/33.32 dB; (d) SRCNN/32.08 dB; (e) SRCNN-Ex/33.30 dB; (f) proposed method/34.57 dB
Fig. 9. Change graph of the average PSNR value for proposed algorithm in the Set 5 test set, with the number of iterations
Table 1. Parameter settings for each layer
View tableTable 1. Parameter settings for each layer
Name Size Number Stride Padding Conv1 5×5 64 1 0 Conv2 3×3 32 1 0 Deconv 9×9 1 3 4
Table 2. PSNR and SSIM values on Set 5 test set
View tableTable 2. PSNR and SSIM values on Set 5 test set
Image BI ScSR SRCNN SRCNN-Ex Proposed method PSNR /dB SSIM PSNR /dB SSIM PSNR /dB SSIM PSNR /dB SSIM PSNR /dB SSIM Baby 33.91 0.90 34.29 0.92 34.83 0.92 34.91 0.92 35.04 0.92 Bird 32.57 0.93 34.11 0.92 33.77 0.94 34.03 0.94 35.46 0.95 Butterfly 24.04 0.82 25.58 0.82 25.00 0.83 25.58 0.84 27.91 0.91 Head 32.88 0.80 33.17 0.80 33.42 0.82 33.42 0.82 33.67 0.83 Women 28.56 0.89 29.94 0.91 29.60 0.91 29.91 0.91 31.22 0.93 Average 30.39 0.87 31.42 0.87 31.32 0.88 31.57 0.89 32.66 0.91
Table 3. PSNR and SSIM values on Set 14 test set
View tableTable 3. PSNR and SSIM values on Set 14 test set
Image BI ScSR SRCNN SRCNN-Ex Proposed method PSNR /dB SSIM PSNR /dB SSIM PSNR /dB SSIM PSNR /dB SSIM PSNR /dB SSIM Baboon 23.21 0.54 23.50 0.59 23.52 0.60 23.54 0.60 23.62 0.61 Barbara 26.25 0.75 26.39 0.75 26.76 0.78 26.84 0.78 26.57 0.78 Bridge 24.40 0.65 24.80 0.70 24.89 0.70 24.95 0.70 25.14 0.71 Coastguard 26.55 0.61 27.00 0.65 27.00 0.66 27.08 0.66 27.12 0.66 Comic 23.12 0.70 23.90 0.76 23.77 0.75 23.87 0.75 24.53 0.79 Face 32.82 0.80 33.10 0.81 33.38 0.82 33.40 0.82 33.71 0.83 Flowers 27.23 0.80 28.25 0.83 28.06 0.83 28.27 0.83 29.22 0.85 Foreman 31.16 0.91 32.04 0.91 32.09 0.91 32.01 0.91 33.65 0.94 Lenna 31.68 0.86 32.64 0.87 32.53 0.87 32.78 0.88 33.57 0.88 Man 27.01 0.75 27.76 0.78 27.56 0.78 27.72 0.78 28.33 0.80 Monarch 29.43 0.92 30.71 0.93 30.40 0.93 30.87 0.93 32.78 0.95 Pepper 32.38 0.87 33.32 0.87 32.08 0.88 33.30 0.88 34.57 0.89 Ppt3 23.71 0.87 24.98 0.87 24.34 0.88 25.02 0.89 26.24 0.92 Zebra 26.63 0.80 27.95 0.82 27.74 0.84 28.37 0.84 29.11 0.85
Table 4. Comparison of training times
View tableTable 4. Comparison of training times
Method 1000 times iteration 105 times iteration 2×105 times iteration 8×108 times iteration SRCNN 477 381600000 SRCNN-Ex 1392 1113600000 Proposed method 141 14100 28200
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Ziteng Shi, Zhiren Wang, Rui Wang, Fuquan Ren. Single Image Super-Resolution Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121001
Paper Information
Category: Image Processing
Received: May. 7, 2018
Accepted: Jun. 8, 2018
Published Online: Aug. 1, 2019
The Author Email: Fuquan Ren (renfu_quan@ysu.edu.cn)
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