Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (5): 1579-1587.DOI: 10.11772/j.issn.1001-9081.2023050689
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Lin GUO1,2,3, Kunhu LIU1(), Chenyang MA1, Youxue LAI1, Yingfen XU1
Received:
2023-06-01
Revised:
2023-09-01
Accepted:
2023-09-12
Online:
2023-09-14
Published:
2024-05-10
Contact:
Kunhu LIU
About author:
GUO Lin, born in 1978, Ph. D., associate professor. Her research interests include signal processing, machine vision, deep learning.Supported by:
郭琳1,2,3, 刘坤虎1(), 马晨阳1, 来佑雪1, 徐映芬1
通讯作者:
刘坤虎
作者简介:
郭琳(1978—),女,湖北随州人,副教授,博士,主要研究方向:信号处理、机器视觉、深度学习基金资助:
CLC Number:
Lin GUO, Kunhu LIU, Chenyang MA, Youxue LAI, Yingfen XU. Image super-resolution reconstruction based on residual attention network with receptive field expansion[J]. Journal of Computer Applications, 2024, 44(5): 1579-1587.
郭琳, 刘坤虎, 马晨阳, 来佑雪, 徐映芬. 基于感受野扩展残差注意力网络的图像超分辨率重建[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1579-1587.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050689
Input | RDAN1 | RDAN2 | RDAN3 | RDAN4 | Concat | Conv |
---|---|---|---|---|---|---|
64 | 64 | 64 | 64 | 64 | 256 | 64 |
Tab. 1 Number of channels in each layer of RF model
Input | RDAN1 | RDAN2 | RDAN3 | RDAN4 | Concat | Conv |
---|---|---|---|---|---|---|
64 | 64 | 64 | 64 | 64 | 256 | 64 |
SA | CA | Concatenate | Conv | ||||
---|---|---|---|---|---|---|---|
输入 | GN | Conv | 输入 | GAP | Conv1d | ||
32 | 32 | 32 | 32 | 32 | 32 | 64 | 64 |
Tab. 2 Number of channels in each layer of SCA model
SA | CA | Concatenate | Conv | ||||
---|---|---|---|---|---|---|---|
输入 | GN | Conv | 输入 | GAP | Conv1d | ||
32 | 32 | 32 | 32 | 32 | 32 | 64 | 64 |
尺度 | 模型 | Set5 | Set14 | BSD100 | Urban100 | ||||
---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | ||
×2 | Bicubic | 33.66 | 0.929 9 | 30.24 | 0.868 8 | 29.56 | 0.843 1 | 26.88 | 0.840 3 |
SRCNN[ | 36.66 | 0.954 2 | 32.45 | 0.906 7 | 31.36 | 0.887 9 | 29.50 | 0.894 6 | |
DRCN[ | 37.63 | 0.958 8 | 33.04 | 0.911 8 | 31.85 | 0.894 2 | 30.75 | 0.913 3 | |
SRFBN-S[ | 37.78 | 0.955 7 | 33.35 | 0.915 6 | 32.00 | 0.897 0 | 31.41 | 0.920 7 | |
CARN[ | 37.76 | 0.959 0 | 33.52 | 0.916 6 | 32.09 | 0.897 8 | 31.92 | 0.925 6 | |
RFDN[ | 38.05 | 0.960 6 | 33.68 | 0.918 4 | 32.16 | 0.899 4 | 32.12 | 0.927 8 | |
SwinIR[ | 38.14 | 0.961 1 | 33.86 | 0.920 6 | 32.31 | 0.901 2 | 32.76 | 0.934 0 | |
EDSR[ | 38.11 | 0.960 2 | 33.92 | 0.919 5 | 32.32 | 0.901 3 | 32.93 | 0.935 1 | |
RDN[ | 38.24 | 0.921 2 | 32.34 | 0.901 7 | 32.89 | 0.935 3 | |||
MHFN[ | — | — | 33.79 | 0.919 6 | 32.20 | 0.899 8 | 32.40 | 0.930 1 | |
ISRN[ | 0.961 3 | 33.84 | |||||||
本文RFE-RAN | 38.19 | 0.961 7 | 34.05 | 0.921 3 | 32.38 | 0.902 8 | 32.97 | 0.936 5 | |
×3 | Bicubic | 30.39 | 0.868 2 | 27.55 | 0.774 2 | 27.21 | 0.738 5 | 24.46 | 0.734 9 |
SRCNN[ | 32.75 | 0.909 0 | 29.30 | 0.821 5 | 28.41 | 0.786 3 | 26.24 | 0.798 9 | |
DRCN[ | 33.82 | 0.922 6 | 29.76 | 0.831 1 | 28.80 | 0.796 3 | 27.15 | 0.827 6 | |
SRFBN-S[ | 34.20 | 0.925 5 | 30.10 | 0.837 2 | 28.96 | 0.801 0 | 27.66 | 0.841 5 | |
CARN[ | 34.29 | 0.925 5 | 30.29 | 0.840 7 | 29.06 | 0.803 4 | 27.38 | 0.840 4 | |
RFDN[ | 34.41 | 0.927 3 | 30.34 | 0.842 0 | 29.09 | 0.805 0 | 28.21 | 0.852 5 | |
SwinIR[ | 34.62 | 0.928 9 | 30.54 | 0.846 3 | 29.20 | 0.808 2 | 28.66 | 0.862 4 | |
EDSR[ | 34.65 | 0.928 0 | 30.52 | 0.846 2 | 29.25 | 0.809 3 | 28.80 | 0.865 3 | |
RDN[ | 34.71 | 30.57 | 0.846 8 | 0.809 3 | 28.80 | 0.865 3 | |||
MHFN[ | — | — | 30.40 | 0.842 8 | 29.13 | 0.805 6 | 28.35 | 0.855 7 | |
ISRN[ | 0.929 4 | 29.25 | |||||||
本文RFE-RAN | 34.71 | 0.929 9 | 30.62 | 0.847 9 | 29.29 | 0.811 4 | 28.89 | 0.867 3 | |
×4 | Bicubic | 28.42 | 0.810 4 | 26.00 | 0.702 7 | 25.96 | 0.667 5 | 23.14 | 0.657 7 |
SRCNN[ | 30.48 | 0.862 8 | 27.50 | 0.751 3 | 26.90 | 0.710 1 | 24.52 | 0.722 1 | |
DRCN[ | 31.53 | 0.885 4 | 28.02 | 0.767 0 | 27.23 | 0.723 3 | 25.14 | 0.751 0 | |
SRFBN-S[ | 31.98 | 0.892 3 | 28.45 | 0.777 9 | 27.44 | 0.731 3 | 25.71 | 0.771 9 | |
CARN[ | 32.13 | 0.893 7 | 28.60 | 0.780 6 | 27.58 | 0.734 9 | 26.07 | 0.783 7 | |
RFDN[ | 32.24 | 0.895 2 | 28.61 | 0.781 9 | 27.57 | 0.736 0 | 26.11 | 0.785 8 | |
SwinIR[ | 32.44 | 0.897 6 | 28.77 | 0.785 8 | 27.69 | 0.740 6 | 26.47 | 0.798 0 | |
EDSR[ | 32.46 | 0.896 8 | 28.80 | 0.787 6 | 27.71 | 0.742 0 | |||
RDN[ | 32.47 | 0.899 0 | 0.787 1 | 27.72 | 0.741 9 | 26.61 | 0.802 8 | ||
MHFN[ | — | — | 28.66 | 0.783 0 | 27.61 | 0.737 1 | 26.27 | 0.790 9 | |
ISRN[ | 32.55 | 28.79 | |||||||
本文RFE-RAN | 0.899 7 | 28.82 | 0.787 6 | 27.75 | 0.743 9 | 26.71 | 0.805 2 |
Tab. 3 Comparison of average PSNR and SSIM values for comparison models on four experimental datasets
尺度 | 模型 | Set5 | Set14 | BSD100 | Urban100 | ||||
---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | ||
×2 | Bicubic | 33.66 | 0.929 9 | 30.24 | 0.868 8 | 29.56 | 0.843 1 | 26.88 | 0.840 3 |
SRCNN[ | 36.66 | 0.954 2 | 32.45 | 0.906 7 | 31.36 | 0.887 9 | 29.50 | 0.894 6 | |
DRCN[ | 37.63 | 0.958 8 | 33.04 | 0.911 8 | 31.85 | 0.894 2 | 30.75 | 0.913 3 | |
SRFBN-S[ | 37.78 | 0.955 7 | 33.35 | 0.915 6 | 32.00 | 0.897 0 | 31.41 | 0.920 7 | |
CARN[ | 37.76 | 0.959 0 | 33.52 | 0.916 6 | 32.09 | 0.897 8 | 31.92 | 0.925 6 | |
RFDN[ | 38.05 | 0.960 6 | 33.68 | 0.918 4 | 32.16 | 0.899 4 | 32.12 | 0.927 8 | |
SwinIR[ | 38.14 | 0.961 1 | 33.86 | 0.920 6 | 32.31 | 0.901 2 | 32.76 | 0.934 0 | |
EDSR[ | 38.11 | 0.960 2 | 33.92 | 0.919 5 | 32.32 | 0.901 3 | 32.93 | 0.935 1 | |
RDN[ | 38.24 | 0.921 2 | 32.34 | 0.901 7 | 32.89 | 0.935 3 | |||
MHFN[ | — | — | 33.79 | 0.919 6 | 32.20 | 0.899 8 | 32.40 | 0.930 1 | |
ISRN[ | 0.961 3 | 33.84 | |||||||
本文RFE-RAN | 38.19 | 0.961 7 | 34.05 | 0.921 3 | 32.38 | 0.902 8 | 32.97 | 0.936 5 | |
×3 | Bicubic | 30.39 | 0.868 2 | 27.55 | 0.774 2 | 27.21 | 0.738 5 | 24.46 | 0.734 9 |
SRCNN[ | 32.75 | 0.909 0 | 29.30 | 0.821 5 | 28.41 | 0.786 3 | 26.24 | 0.798 9 | |
DRCN[ | 33.82 | 0.922 6 | 29.76 | 0.831 1 | 28.80 | 0.796 3 | 27.15 | 0.827 6 | |
SRFBN-S[ | 34.20 | 0.925 5 | 30.10 | 0.837 2 | 28.96 | 0.801 0 | 27.66 | 0.841 5 | |
CARN[ | 34.29 | 0.925 5 | 30.29 | 0.840 7 | 29.06 | 0.803 4 | 27.38 | 0.840 4 | |
RFDN[ | 34.41 | 0.927 3 | 30.34 | 0.842 0 | 29.09 | 0.805 0 | 28.21 | 0.852 5 | |
SwinIR[ | 34.62 | 0.928 9 | 30.54 | 0.846 3 | 29.20 | 0.808 2 | 28.66 | 0.862 4 | |
EDSR[ | 34.65 | 0.928 0 | 30.52 | 0.846 2 | 29.25 | 0.809 3 | 28.80 | 0.865 3 | |
RDN[ | 34.71 | 30.57 | 0.846 8 | 0.809 3 | 28.80 | 0.865 3 | |||
MHFN[ | — | — | 30.40 | 0.842 8 | 29.13 | 0.805 6 | 28.35 | 0.855 7 | |
ISRN[ | 0.929 4 | 29.25 | |||||||
本文RFE-RAN | 34.71 | 0.929 9 | 30.62 | 0.847 9 | 29.29 | 0.811 4 | 28.89 | 0.867 3 | |
×4 | Bicubic | 28.42 | 0.810 4 | 26.00 | 0.702 7 | 25.96 | 0.667 5 | 23.14 | 0.657 7 |
SRCNN[ | 30.48 | 0.862 8 | 27.50 | 0.751 3 | 26.90 | 0.710 1 | 24.52 | 0.722 1 | |
DRCN[ | 31.53 | 0.885 4 | 28.02 | 0.767 0 | 27.23 | 0.723 3 | 25.14 | 0.751 0 | |
SRFBN-S[ | 31.98 | 0.892 3 | 28.45 | 0.777 9 | 27.44 | 0.731 3 | 25.71 | 0.771 9 | |
CARN[ | 32.13 | 0.893 7 | 28.60 | 0.780 6 | 27.58 | 0.734 9 | 26.07 | 0.783 7 | |
RFDN[ | 32.24 | 0.895 2 | 28.61 | 0.781 9 | 27.57 | 0.736 0 | 26.11 | 0.785 8 | |
SwinIR[ | 32.44 | 0.897 6 | 28.77 | 0.785 8 | 27.69 | 0.740 6 | 26.47 | 0.798 0 | |
EDSR[ | 32.46 | 0.896 8 | 28.80 | 0.787 6 | 27.71 | 0.742 0 | |||
RDN[ | 32.47 | 0.899 0 | 0.787 1 | 27.72 | 0.741 9 | 26.61 | 0.802 8 | ||
MHFN[ | — | — | 28.66 | 0.783 0 | 27.61 | 0.737 1 | 26.27 | 0.790 9 | |
ISRN[ | 32.55 | 28.79 | |||||||
本文RFE-RAN | 0.899 7 | 28.82 | 0.787 6 | 27.75 | 0.743 9 | 26.71 | 0.805 2 |
模型 | 参数量/MB | PSNR/dB | 模型 | 参数量/MB | PSNR/dB |
---|---|---|---|---|---|
EDSR | 43.08 | 32.32 | MHFN | 1.43 | 32.20 |
RDN | 22.30 | 32.34 | RFE-RAN | 15.63 | 32.38 |
ISRN | 3.50 | 32.35 |
Tab. 4 Number of parameters and PSNR of each model
模型 | 参数量/MB | PSNR/dB | 模型 | 参数量/MB | PSNR/dB |
---|---|---|---|---|---|
EDSR | 43.08 | 32.32 | MHFN | 1.43 | 32.20 |
RDN | 22.30 | 32.34 | RFE-RAN | 15.63 | 32.38 |
ISRN | 3.50 | 32.35 |
模型编号 | 模块数 | 参数量/MB | PSNR/dB | ||
---|---|---|---|---|---|
MM | RF | Set14 | Urban100 | ||
1 | 4 | 8 | 6.08 | 33.77 | 32.68 |
2 | 8 | 8 | 12.24 | 33.82 | 32.76 |
3 | 4 | 16 | 13.10 | 33.83 | 32.79 |
4 | 16 | 4 | 12.41 | 33.92 | 32.92 |
5 | 20 | 4 | 15.63 | 33.93 | 32.94 |
Tab. 5 Number of parameters and PSNR of model with different blocks
模型编号 | 模块数 | 参数量/MB | PSNR/dB | ||
---|---|---|---|---|---|
MM | RF | Set14 | Urban100 | ||
1 | 4 | 8 | 6.08 | 33.77 | 32.68 |
2 | 8 | 8 | 12.24 | 33.82 | 32.76 |
3 | 4 | 16 | 13.10 | 33.83 | 32.79 |
4 | 16 | 4 | 12.41 | 33.92 | 32.92 |
5 | 20 | 4 | 15.63 | 33.93 | 32.94 |
模型编号 | CA | SA | RFE | PSNR/dB |
---|---|---|---|---|
1 | × | × | × | 30.21 |
2 | √ | × | × | 30.16 |
3 | × | √ | × | 30.28 |
4 | √ | √ | × | 30.37 |
Tab. 6 Ablation experiment results
模型编号 | CA | SA | RFE | PSNR/dB |
---|---|---|---|---|
1 | × | × | × | 30.21 |
2 | √ | × | × | 30.16 |
3 | × | √ | × | 30.28 |
4 | √ | √ | × | 30.37 |
数据集 | 尺度 | RAN | RFE-RAN | ||
---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | ||
Set5 | ×2 | 38.15 | 0.961 6 | 38.19 | 0.961 7 |
×3 | 34.68 | 0.929 7 | 34.71 | 0.929 9 | |
×4 | 32.53 | 0.899 9 | 32.54 | 0.899 7 | |
Set14 | ×2 | 33.93 | 0.921 2 | 34.05 | 0.921 3 |
×3 | 30.55 | 0.846 6 | 30.62 | 0.847 9 | |
×4 | 28.81 | 0.787 4 | 28.82 | 0.787 6 | |
BSD100 | ×2 | 32.29 | 0.901 7 | 32.38 | 0.902 8 |
×3 | 29.26 | 0.810 5 | 29.29 | 0.811 4 | |
×4 | 27.72 | 0.742 6 | 27.75 | 0.743 9 | |
Urban100 | ×2 | 32.73 | 0.933 7 | 32.97 | 0.936 5 |
×3 | 28.77 | 0.864 1 | 28.89 | 0.867 3 | |
×4 | 26.60 | 0.801 2 | 26.70 | 0.805 2 |
Tab. 7 Influence of RFE module to proposed model on different datasets
数据集 | 尺度 | RAN | RFE-RAN | ||
---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | ||
Set5 | ×2 | 38.15 | 0.961 6 | 38.19 | 0.961 7 |
×3 | 34.68 | 0.929 7 | 34.71 | 0.929 9 | |
×4 | 32.53 | 0.899 9 | 32.54 | 0.899 7 | |
Set14 | ×2 | 33.93 | 0.921 2 | 34.05 | 0.921 3 |
×3 | 30.55 | 0.846 6 | 30.62 | 0.847 9 | |
×4 | 28.81 | 0.787 4 | 28.82 | 0.787 6 | |
BSD100 | ×2 | 32.29 | 0.901 7 | 32.38 | 0.902 8 |
×3 | 29.26 | 0.810 5 | 29.29 | 0.811 4 | |
×4 | 27.72 | 0.742 6 | 27.75 | 0.743 9 | |
Urban100 | ×2 | 32.73 | 0.933 7 | 32.97 | 0.936 5 |
×3 | 28.77 | 0.864 1 | 28.89 | 0.867 3 | |
×4 | 26.60 | 0.801 2 | 26.70 | 0.805 2 |
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