Chinese Optics, Volume. 16, Issue 5, 1034(2023)
Image super-resolution reconstruction with multi-scale attention fusion
Fig. 5. Comparison of the results of "zebra" 3× in the Set14 dataset
Fig. 6. Comparison of the results of "148026" 4× in the B100 dataset
Fig. 7. Comparison of the results of "img012" 4× in the Urban100 dataset
Fig. 8. PSNR and parameters of different models on the Set5(×4) dataset
Table 1. Parameters of the multi-scale feature extraction units
View tableView in ArticleTable 1. Parameters of the multi-scale feature extraction units
所属 模块 组件名 卷积核 大小 输入尺寸 输出尺寸 第一级 Conv1 1×1 H×W×64 H×W×32 Conv3 3×3 H×W×32 H×W×32 第二级 Conv3 3×3 H×W×64 H×W×64 通道注意力 Fusion 1×1 H×W×192 H×W×64 Pooling — H×W×64 1×1×64 Conv1-1 1×1 1×1×64 1×1×4 Conv1-2 1×1 1×1×4 1×1×64
Table 2. Validation of different modules
View tableView in ArticleTable 2. Validation of different modules
模型名字 CA RB FFRL PSNR/SSIM/TIME MSARNSC × √ × 27.62/0.7682/ 0.11s MSARNDB × √ × 27.67/0.7751/0.16s MSARNIB × √ × 27.78/0.7767/0.13s MSARNFFRL- √ √ × 28.26/0.7789/0.15s MSARN √ √ √ 28.64 /0.7840/0.14s
Table 3. Validation of residual branch and channel attention
View tableView in ArticleTable 3. Validation of residual branch and channel attention
模块名字 CA RB FFRL PSNR/SSIM MSARNRB- √ × √ 28.57/0.7802 MSARNCA- × √ √ 28.35/0.7778 MSARN √ √ √ 28.64 /0.7840
Table 4. PSNR comparison of different loss functions
View tableView in ArticleTable 4. PSNR comparison of different loss functions
放大比例 损失函数 Set5 Set14 ×2 L2 37.84 33.50 Charbonnier 38.13 33.89 ×3 L2 33.91 30.03 Charbonnier 34.05 30.40 ×4 L2 31.53 28.26 Charbonnier 31.67 28.41
Table 5. PSNR/SSIM comparison of different super-resolution models
View tableView in ArticleTable 5. PSNR/SSIM comparison of different super-resolution models
放大比例 方法 Set5 Set14 BSD100 Urban100 ×2 Bicubic 33.68/0.9265 30.24/0.8691 29.56/0.8435 26.88/0.8405 SRCNN 36.66/0.9542 32.45/0.9067 31.56/0.8879 29.51/0.8946 VDSR 37.52/0.9587 33.05/0.9127 31.90/0.8960 30.77/0.9141 DRRN 37.74/0.9597 33.23/0.9136 32.05/0.8973 31.23/0.9188 IDN 37.83/0.9600 33.30/0.9148 32.08/0.8985 31.27/0.9196 MSRN 38.08/0.9605 33.74 /0.917032.23/0.9013 32.22/0.9326 PAN 38.00/0.9605 33.59/ 0.9181 32.18/0.8997 32.01/0.9273 EFDN 38.00/0.9604 33.57/0.9179 32.18/0.8998 32.05/0.9275 本文 38.43/0.9626 34.05/0.9213 32.32/0.9028 32.28/0.9338 ×3 Bicubic 30.40/0.8686 27.54/0.7741 27.21/0.7389 24.46/0.7349 SRCNN 32.75/0.9090 29.29/0.8215 28.41/0.7863 26.24/0.7991 VDSR 33.66/0.9213 29.78/0.8318 28.83/0.7976 27.14/0.8279 DRRN 34.03/0.9244 29.96/0.8349 28.95/0.8004 27.53/0.8377 IDN 34.11/0.9253 29.99/0.8354 28.95/0.8013 27.42/0.8359 MSRN 34.38/0.9262 30.34/0.8395 29.08/0.8041 28.08/ 0.8554 PAN 34.40/0.9271 30.36 /0.8423 29.11/0.8050 28.11 /0.8511本文 34.61/0.9284 30.33 /0.8480 29.25/0.8076 28.39/0.8607 ×4 Bicubic 28.43/0.8109 26.00/0.7023 25.96/0.6678 23.14/0.6574 SRCNN 30.48/0.8628 27.50/0.7513 26.90/0.7103 24.52/0.7226 VDSR 31.35/0.8838 28.02/0.7678 27.29/0.7252 25.18/0.7525 DRRN 31.68/0.8888 28.21/0.7720 27.38/0.7284 25.44/0.7638 IDN 31.82/0.8903 28.25/0.7730 27.41/0.7297 25.41/0.7632 MSRN 32.07/0.8903 28.60/0.7751 27.52/0.7273 26.04/ 0.7896 PAN 32.13/0.8948 28.61/0.7822 27.59/0.7363 26.11/0.7854 EFDN 32.08/0.8931 28.58/0.7809 27.56/0.7354 26.00 /0.7815本文 32.52/0.8992 28.85/0.7840 27.70/0.7410 26.21/ 0.7866
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Chun-yi CHEN, Xin-yi WU, Xiao-juan HU, Hai-yang YU. Image super-resolution reconstruction with multi-scale attention fusion[J]. Chinese Optics, 2023, 16(5): 1034
Category: Original Article
Received: Jan. 28, 2023
Accepted: Apr. 4, 2023
Published Online: Oct. 27, 2023
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