《计算机应用》唯一官方网站 ›› 2024, Vol. 44 ›› Issue (5): 1579-1587.DOI: 10.11772/j.issn.1001-9081.2023050689
• 多媒体计算与计算机仿真 • 上一篇
郭琳1,2,3, 刘坤虎1(), 马晨阳1, 来佑雪1, 徐映芬1
收稿日期:
2023-06-01
修回日期:
2023-09-01
接受日期:
2023-09-12
发布日期:
2023-09-14
出版日期:
2024-05-10
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:
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:
摘要:
针对现有残差网络存在残差特征利用不充分、细节丢失的问题,提出一种结合两层残差聚合结构和感受野扩展双注意力机制的深度神经网络模型,用于单幅图像超分辨率(SISR)重建。该模型通过跳跃连接形成两层嵌套的残差聚合网络结构,对网络各层提取的大量残差信息进行分层聚集和融合,能减少包含图像细节的残差信息的丢失。同时,设计一种多尺度感受野扩展模块,能捕获更大范围、不同尺度的上下文相关信息,促进深层残差特征的有效提取;并引入空间-通道双注意力机制,增强残差网络的判别性学习能力,提高重建图像质量。在数据集Set5、Set14、BSD100和Urban100上进行重建实验,并从客观指标和主观视觉效果上将所提模型与主流模型进行比较。客观评价结果表明,所提模型在全部4个测试数据集上均优于对比模型,其中,相较于经典的超分辨率卷积神经网络(SRCNN)模型和性能次优的对比模型ISRN(Iterative Super-Resolution Network),在放大2倍、3倍、4倍时的平均峰值信噪比(PSNR)分别提升1.91、1.71、1.61 dB和0.06、0.04、0.04 dB;视觉效果对比显示,所提模型恢复的图像细节纹理更清晰。
中图分类号:
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, 2024, 44(5): 1579-1587.
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.
Input | RDAN1 | RDAN2 | RDAN3 | RDAN4 | Concat | Conv |
---|---|---|---|---|---|---|
64 | 64 | 64 | 64 | 64 | 256 | 64 |
表1 RF模块中各层的通道数
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 |
表2 SCA模块中各层的通道数
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 |
表3 各对比模型在4个实验数据集上的平均PSNR和SSIM值对比
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 |
表4 各模型的参数量及PSNR
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 |
表5 不同模块数下模型的参数量和PSNR
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 |
表6 消融实验结果
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 |
表7 RFE模块对本文模型在不同数据集上性能的影响
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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