电子学报 ›› 2022, Vol. 50 ›› Issue (7): 1753-1765.DOI: 10.12263/DZXB.20210854
• 学术论文 • 上一篇
王相海1,2, 赵晓阳1, 王鑫莹2, 赵克云2, 宋传鸣2
收稿日期:
2021-07-06
修回日期:
2022-03-29
出版日期:
2022-07-25
发布日期:
2022-07-30
作者简介:
基金资助:
WANG Xiang-hai1,2, ZHAO Xiao-yang1, WANG Xin-ying2, ZHAO Ke-yun2, SONG Chuan-ming2
Received:
2021-07-06
Revised:
2022-03-29
Online:
2022-07-25
Published:
2022-07-30
摘要:
图像超分辨率重建作为一个典型的非适定问题一直受到重视,尽管近年来出现了许多行之有效的卷积神经网络超分辨率重建模型,但如何全面挖掘图像先验信息,用以提高重建图像的细节清晰度仍有待深入研究.本文提出一种基于非抽取Wavelet变换的边缘学习深度残差网络单幅图像超分辨重建模型NDW-EDRN(Non-Decimated Wavelet Edge learning using Deep Residual Networks),在图像经非抽取Wavelet变换后获得多冗余信息、平滑及梯度值较小的低频区域和边缘及梯度值较大的高频区域的基础上,将整体网络框架设计为采用不同结构的CNN(Convolutional Neural Networks)模型来对低频子带与高频子带分别进行学习的策略:对低频子带采用稠密跳跃连接的方式整体性学习低频子带间的映射关系;对高频子带采用一种新型的U-net模型,将图像退化过程中所丢失的边缘作为网络的期望输出,通过基于块的跳跃连接来使网络更精细地学习缺失性边缘,从而更加充分、有效地获取图像在退化过程中所丢失的边缘细节信息.大量实验结果表明,该网络模型能够有效提高重建图像的质量,特别在恢复低分辨率图像的边缘信息方面具有一定的优势,在一定程度上弥补了传统CNN网络模型捕捉图像细节信息的不足.
中图分类号:
王相海, 赵晓阳, 王鑫莹, 赵克云, 宋传鸣. 非抽取小波边缘学习深度残差网络的单幅图像超分辨率重建[J]. 电子学报, 2022, 50(7): 1753-1765.
WANG Xiang-hai, ZHAO Xiao-yang, WANG Xin-ying, ZHAO Ke-yun, SONG Chuan-ming. Single Image Super-Resolution Reconstruction Using Deep Residual Networks with Non-decimated Wavelet Edge Learning[J]. Acta Electronica Sinica, 2022, 50(7): 1753-1765.
低频 | 高频 | |||
---|---|---|---|---|
高分 子带 | ![]() | ![]() | ![]() | ![]() |
高分 直方图 | ![]() | ![]() | ![]() | ![]() |
低分 子带 | ![]() | ![]() | ![]() | ![]() |
低分 直方图 | ![]() | ![]() | ![]() | ![]() |
差分 子带 | ![]() | ![]() | ![]() | ![]() |
差分 直方图 | ![]() | ![]() | ![]() | ![]() |
(b) butterfly图像 |
图3 图像NDWT子带、差分子带及其直方图统计
低频 | 高频 | |||
---|---|---|---|---|
高分 子带 | ![]() | ![]() | ![]() | ![]() |
高分 直方图 | ![]() | ![]() | ![]() | ![]() |
低分 子带 | ![]() | ![]() | ![]() | ![]() |
低分 直方图 | ![]() | ![]() | ![]() | ![]() |
差分 子带 | ![]() | ![]() | ![]() | ![]() |
差分 直方图 | ![]() | ![]() | ![]() | ![]() |
(b) butterfly图像 |
数据集 | 倍数 | 非多尺度深度学习模型(层数≤20) | 多尺度深度学习模型 | 本文模型 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SRCNN | VDSR | SRMD | DSRN | DWSR | WaveResNet | NDW-EDRN | |||||||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | ||
Set5 | 36.66 | 0.954 | 37.53 | 37.53 | 37.43 | 0.957 | 37.57 | 37.77 | 0.960 | ||||||
30.04 | 0.861 | 31.35 | 0.884 | 31.59 | 31.40 | 0.883 | 31.39 | 0.883 | 31.52 | 0.886 | 31.72 | 0.889 | |||
Set14 | 32.42 | 0.906 | 33.03 | 0.912 | 33.12 | 0.914 | 0.913 | 33.07 | 0.917 | 33.09 | 0.913 | 33.26 | |||
27.49 | 0.750 | 28.01 | 0.767 | 0.772 | 28.07 | 28.04 | 0.767 | 28.11 | 28.27 | 0.772 | |||||
Urban100 | 29.50 | 0.895 | 30.76 | 0.914 | 30.89 | 0.916 | 30.97 | 0.916 | 30.46 | 0.916 | 30.96 | 0.917 | 31.30 | 0.926 | |
24.52 | 0.722 | 25.18 | 0.752 | 25.34 | 0.761 | 25.08 | 0.747 | 25.26 | 0.755 | 0.761 | 25.51 | 0.763 | |||
Average | 32.86 | 0.918 | 33.77 | 0.928 | 33.85 | 0.929 | 33.65 | 0.928 | 33.87 | 34.11 | 0.933 | ||||
27.50 | 0.778 | 28.18 | 0.801 | 28.18 | 0.800 | 28.23 | 0.802 | 28.33 | 0.806 | 28.50 | 0.808 |
表1 重建图像的定量比较1
数据集 | 倍数 | 非多尺度深度学习模型(层数≤20) | 多尺度深度学习模型 | 本文模型 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SRCNN | VDSR | SRMD | DSRN | DWSR | WaveResNet | NDW-EDRN | |||||||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | ||
Set5 | 36.66 | 0.954 | 37.53 | 37.53 | 37.43 | 0.957 | 37.57 | 37.77 | 0.960 | ||||||
30.04 | 0.861 | 31.35 | 0.884 | 31.59 | 31.40 | 0.883 | 31.39 | 0.883 | 31.52 | 0.886 | 31.72 | 0.889 | |||
Set14 | 32.42 | 0.906 | 33.03 | 0.912 | 33.12 | 0.914 | 0.913 | 33.07 | 0.917 | 33.09 | 0.913 | 33.26 | |||
27.49 | 0.750 | 28.01 | 0.767 | 0.772 | 28.07 | 28.04 | 0.767 | 28.11 | 28.27 | 0.772 | |||||
Urban100 | 29.50 | 0.895 | 30.76 | 0.914 | 30.89 | 0.916 | 30.97 | 0.916 | 30.46 | 0.916 | 30.96 | 0.917 | 31.30 | 0.926 | |
24.52 | 0.722 | 25.18 | 0.752 | 25.34 | 0.761 | 25.08 | 0.747 | 25.26 | 0.755 | 0.761 | 25.51 | 0.763 | |||
Average | 32.86 | 0.918 | 33.77 | 0.928 | 33.85 | 0.929 | 33.65 | 0.928 | 33.87 | 34.11 | 0.933 | ||||
27.50 | 0.778 | 28.18 | 0.801 | 28.18 | 0.800 | 28.23 | 0.802 | 28.33 | 0.806 | 28.50 | 0.808 |
数据集 | 倍数 | 生成对抗网络模型 | 非多尺度深度学习模型(层数>50) | 本文模型 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SRGAN | SRPGAN | DRRN_B1U25 | LapSRNss-D5R8 | MemNet | NDW-EDRN | ||||||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | ||
Set5 | — | — | 29.67 | 0.950 | 37.74 | 37.72 | 0.960 | 37.78 | 0.960 | 0.960 | |||
29.40 | 0.847 | 22.68 | 0.880 | 31.68 | 0.889 | 31.74 | 31.74 | 0.889 | 0.889 | ||||
Set14 | — | — | 27.66 | 33.23 | 0.914 | 33.24 | 0.914 | 33.28 | 0.914 | 0.914 | |||
26.02 | 0.740 | 22.50 | 0.786 | 28.21 | 0.772 | 28.25 | 0.772 | 28.27 | 0.772 | ||||
Urban100 | — | — | 30.41 | 0.892 | 31.23 | 0.919 | 31.01 | 0.917 | 31.31 | 0.920 | 0.926 | ||
— | — | 20.00 | 0.763 | 25.44 | 25.45 | 0.765 | 0.763 | 25.51 | 0.763 | ||||
Average | — | — | 20.91 | 0.918 | 34.07 | 33.67 | 0.927 | 34.12 | 0.933 | ||||
— | — | 21.73 | 0.810 | 28.44 | 28.31 | 0.804 | 28.50 | 28.50 |
表2 重建图像的定量比较2
数据集 | 倍数 | 生成对抗网络模型 | 非多尺度深度学习模型(层数>50) | 本文模型 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SRGAN | SRPGAN | DRRN_B1U25 | LapSRNss-D5R8 | MemNet | NDW-EDRN | ||||||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | ||
Set5 | — | — | 29.67 | 0.950 | 37.74 | 37.72 | 0.960 | 37.78 | 0.960 | 0.960 | |||
29.40 | 0.847 | 22.68 | 0.880 | 31.68 | 0.889 | 31.74 | 31.74 | 0.889 | 0.889 | ||||
Set14 | — | — | 27.66 | 33.23 | 0.914 | 33.24 | 0.914 | 33.28 | 0.914 | 0.914 | |||
26.02 | 0.740 | 22.50 | 0.786 | 28.21 | 0.772 | 28.25 | 0.772 | 28.27 | 0.772 | ||||
Urban100 | — | — | 30.41 | 0.892 | 31.23 | 0.919 | 31.01 | 0.917 | 31.31 | 0.920 | 0.926 | ||
— | — | 20.00 | 0.763 | 25.44 | 25.45 | 0.765 | 0.763 | 25.51 | 0.763 | ||||
Average | — | — | 20.91 | 0.918 | 34.07 | 33.67 | 0.927 | 34.12 | 0.933 | ||||
— | — | 21.73 | 0.810 | 28.44 | 28.31 | 0.804 | 28.50 | 28.50 |
测试图像 | GT | Bicubic | SRCNN | LapSRN | VDSR | NDW-EDRN |
---|---|---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 35.65 | 37.06 | 37.23 | 37.38 | ||
Set5中baby图像 | SSIM: | 0.979 8 | 0.985 1 | 0.9860 | ||
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 22.55 | 22.79 | 23.32 | 23.47 | ||
Set14中baboon图像 | SSIM: | 0.794 2 | 0.827 9 | 0.839 1 | 0.8423 | |
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 21.14 | 24.19 | 23.80 | 24.61 | ||
Urban100中img_063图像 | SSIM: | 0.874 0 | 0.913 4 | 0.913 0 | 0.917 8 |
图6 对比其他非多尺度网络模型放大2倍的细节图
测试图像 | GT | Bicubic | SRCNN | LapSRN | VDSR | NDW-EDRN |
---|---|---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 35.65 | 37.06 | 37.23 | 37.38 | ||
Set5中baby图像 | SSIM: | 0.979 8 | 0.985 1 | 0.9860 | ||
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 22.55 | 22.79 | 23.32 | 23.47 | ||
Set14中baboon图像 | SSIM: | 0.794 2 | 0.827 9 | 0.839 1 | 0.8423 | |
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 21.14 | 24.19 | 23.80 | 24.61 | ||
Urban100中img_063图像 | SSIM: | 0.874 0 | 0.913 4 | 0.913 0 | 0.917 8 |
测试图像 | GT | Bicubic | SRCNN | LapSRN | VDSR | NDW-EDRN |
---|---|---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 20.90 | 23.95 | 25.52 | 25.89 | ||
Set5中butterfly图像 | SSIM: | 0.861 4 | 0.925 1 | 0.946 6 | 0.9500 | |
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 28.36 | 29.50 | 29.80 | 29.97 | ||
Set14中lenna图像 | SSIM: | 0.963 2 | 0.970 0 | 0.971 8 | 0.9726 | |
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 21.64 | 23.57 | 24.57 | 25.22 | ||
Urban100中img_087图像 | SSIM: | 0.838 0 | 0.884 5 | 0.905 7 | 0.9161 |
图7 对比非多尺度其他网络模型放大4倍的细节
测试图像 | GT | Bicubic | SRCNN | LapSRN | VDSR | NDW-EDRN |
---|---|---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 20.90 | 23.95 | 25.52 | 25.89 | ||
Set5中butterfly图像 | SSIM: | 0.861 4 | 0.925 1 | 0.946 6 | 0.9500 | |
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 28.36 | 29.50 | 29.80 | 29.97 | ||
Set14中lenna图像 | SSIM: | 0.963 2 | 0.970 0 | 0.971 8 | 0.9726 | |
![]() | ![]() | ![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 21.64 | 23.57 | 24.57 | 25.22 | ||
Urban100中img_087图像 | SSIM: | 0.838 0 | 0.884 5 | 0.905 7 | 0.9161 |
测试图像 | GT | DWSR | WaveResNet | NDW-EDRN |
---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 37.00 | 37.35 | 37.38 | |
Set5中baby图像 | SSIM: | 0.984 0 | 0.986 1 | 0.986 0 |
![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 23.38 | 23.44 | 23.47 | |
Set14中baboon图像 | SSIM: | 0.841 1 | 0.842 3 | 0.842 3 |
![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 24.57 | 24.60 | 24.61 | |
Urban100中img_063图像 | SSIM: | 0.917 2 | 0.917 5 | 0.917 8 |
图8 对比其他多尺度网络模型放大2倍的细节图
测试图像 | GT | DWSR | WaveResNet | NDW-EDRN |
---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 37.00 | 37.35 | 37.38 | |
Set5中baby图像 | SSIM: | 0.984 0 | 0.986 1 | 0.986 0 |
![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 23.38 | 23.44 | 23.47 | |
Set14中baboon图像 | SSIM: | 0.841 1 | 0.842 3 | 0.842 3 |
![]() | ![]() | ![]() | ![]() | ![]() |
PSNR: | 24.57 | 24.60 | 24.61 | |
Urban100中img_063图像 | SSIM: | 0.917 2 | 0.917 5 | 0.917 8 |
测试图像 | GT | DWSR | WaveResNet | NDW-EDRN |
---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | |
PSNR: | 25.36 | — | 25.89 | |
Set5中butterfly图像 | SSIM: | 0.944 4 | — | 0.950 0 |
![]() | ![]() | ![]() | ![]() | |
PSNR: | 29.89 | — | 29.97 | |
Set14中lenna图像 | SSIM: | 0.972 0 | — | 0.972 6 |
![]() | ![]() | ![]() | ![]() | |
PSNR: | 24.85 | — | 25.22 | |
Urban100中img_087图像 | SSIM: | 0.907 6 | — | 0.916 1 |
图9 对比其他多尺度网络模型放大4倍的细节(注:WaveResNet网络未公开四倍的预训练模型)
测试图像 | GT | DWSR | WaveResNet | NDW-EDRN |
---|---|---|---|---|
![]() | ![]() | ![]() | ![]() | |
PSNR: | 25.36 | — | 25.89 | |
Set5中butterfly图像 | SSIM: | 0.944 4 | — | 0.950 0 |
![]() | ![]() | ![]() | ![]() | |
PSNR: | 29.89 | — | 29.97 | |
Set14中lenna图像 | SSIM: | 0.972 0 | — | 0.972 6 |
![]() | ![]() | ![]() | ![]() | |
PSNR: | 24.85 | — | 25.22 | |
Urban100中img_087图像 | SSIM: | 0.907 6 | — | 0.916 1 |
SR_ CNN | VD_ SR | SR_ MD | DR_ RN | Lap_ SRN | Mem_ Net | NSW -EDRN | |
---|---|---|---|---|---|---|---|
Param (k) | 57 | 665 | 1 483 | 297 | 827 | 2 910 | 5 736 |
Flops (G) | 0.475 | 2.73 | 6.08 | 1.22 | 19.13 | 11.96 | 3.79 |
表3 参数与计算量比较
SR_ CNN | VD_ SR | SR_ MD | DR_ RN | Lap_ SRN | Mem_ Net | NSW -EDRN | |
---|---|---|---|---|---|---|---|
Param (k) | 57 | 665 | 1 483 | 297 | 827 | 2 910 | 5 736 |
Flops (G) | 0.475 | 2.73 | 6.08 | 1.22 | 19.13 | 11.96 | 3.79 |
数据集 | 倍数 | 学习模式 | |||||
---|---|---|---|---|---|---|---|
整体式 | 缺失式 | NDW-EDRN | |||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | ||
Set5 | ×2 | 37.69 | 0.952 | 37.74 | 0.957 | 37.77 | 0.960 |
Set14 | 33.14 | 0.909 | 33.23 | 0.912 | 33.26 | 0.914 | |
Urban 100 | 31.22 | 0.921 | 31.26 | 0.925 | 31.30 | 0.926 | |
Aver age | ×2 | 34.02 | 0.927 | 34.08 | 0.931 | 34.11 | 0.933 |
表4 消融实验重建数据集的定量比较
数据集 | 倍数 | 学习模式 | |||||
---|---|---|---|---|---|---|---|
整体式 | 缺失式 | NDW-EDRN | |||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | ||
Set5 | ×2 | 37.69 | 0.952 | 37.74 | 0.957 | 37.77 | 0.960 |
Set14 | 33.14 | 0.909 | 33.23 | 0.912 | 33.26 | 0.914 | |
Urban 100 | 31.22 | 0.921 | 31.26 | 0.925 | 31.30 | 0.926 | |
Aver age | ×2 | 34.02 | 0.927 | 34.08 | 0.931 | 34.11 | 0.933 |
测试 图像 | 学习模式 | |||||
---|---|---|---|---|---|---|
整体式 | 缺失式_s | NDW-EDRN_s | ||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | |
bird | 38.39 | 0.994 7 | 38.46 | 0.995 3 | ||
butter fly | 31.95 | 0.987 4 | 0.987 6 | 32.02 | ||
img 068 | 35.79 | 0.974 1 | 35.88 | 0.974 8 | ||
baby | 36.58 | 0.984 4 | 36.64 | 0.984 6 |
表5 消融实验重建图像的定量比较
测试 图像 | 学习模式 | |||||
---|---|---|---|---|---|---|
整体式 | 缺失式_s | NDW-EDRN_s | ||||
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | |
bird | 38.39 | 0.994 7 | 38.46 | 0.995 3 | ||
butter fly | 31.95 | 0.987 4 | 0.987 6 | 32.02 | ||
img 068 | 35.79 | 0.974 1 | 35.88 | 0.974 8 | ||
baby | 36.58 | 0.984 4 | 36.64 | 0.984 6 |
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