Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2280-2287.DOI: 10.11772/j.issn.1001-9081.2022060877
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Min LIANG(), Jiayi LIU, Jie LI
Received:
2022-06-16
Revised:
2022-09-06
Accepted:
2022-09-08
Online:
2022-10-18
Published:
2023-07-10
Contact:
Min LIANG
About author:
LIANG Min, born in 1979, Ph. D., associate professor. Her research interests include image processing, pattern recognition.Supported by:
通讯作者:
梁敏
作者简介:
梁敏(1979—),女,山西忻州人,副教授,博士,CCF会员,主要研究方向:图像处理、模式识别;基金资助:
CLC Number:
Min LIANG, Jiayi LIU, Jie LI. Image super-resolution reconstruction method based on iterative feedback and attention mechanism[J]. Journal of Computer Applications, 2023, 43(7): 2280-2287.
梁敏, 刘佳艺, 李杰. 融合迭代反馈与注意力机制的图像超分辨重建方法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2280-2287.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060877
频率分解模块 | 注意力模块 | PSNR/dB |
---|---|---|
× | × | 26.80 |
× | √ | 26.89 |
√ | × | 26.85 |
√ | √ | 26.90 |
Tab. 1 Experimental results of different model structures on test set (α=8)
频率分解模块 | 注意力模块 | PSNR/dB |
---|---|---|
× | × | 26.80 |
× | √ | 26.89 |
√ | × | 26.85 |
√ | √ | 26.90 |
λ值 | PSNR/dB | λ值 | PSNR/dB |
---|---|---|---|
0 | 26.30 | 1 | 25.12 |
0.01 | 27.08 | 10 | 23.41 |
0.1 | 27.03 |
Tab. 2 Experimental results for different λ values on General100 dataset (α=4)
λ值 | PSNR/dB | λ值 | PSNR/dB |
---|---|---|---|
0 | 26.30 | 1 | 25.12 |
0.01 | 27.08 | 10 | 23.41 |
0.1 | 27.03 |
算法 | 放大 倍数 | Set5 | Set14 | BSD100 | Urban100 | Manga109 | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | ||
Bicubic | 2× | 33.65 | 0.930 | 30.24 | 0.869 | 29.56 | 0.844 | 26.88 | 0.841 | 30.84 | 0.935 |
SRCNN[ | 36.66 | 0.954 | 32.45 | 0.906 | 31.36 | 0.888 | 29.52 | 0.895 | 35.72 | 0.968 | |
FSRCNN[ | 37.00 | 0.956 | 32.63 | 0.909 | 31.50 | 0.891 | 29.88 | 0.902 | — | — | |
VDSR[ | 37.74 | 0.959 | 32.97 | 0.913 | 31.90 | 0.896 | 30.77 | 0.914 | 37.16 | 0.974 | |
LapSRN[ | 37.52 | 0.929 | 33.08 | 0.913 | 31.80 | 0.895 | 31.05 | 0.910 | 37.53 | 0.974 | |
ADSR[ | 37.36 | 0.958 | 32.86 | 0.911 | 31.78 | 0.894 | 30.44 | 0.910 | — | — | |
DBPN[ | 37.60 | 0.959 | 33.18 | 0.914 | 31.94 | 0.897 | 31.20 | 0.918 | 37.57 | 0.974 | |
IFANet(本文方法) | 37.70 | 0.959 | 33.24 | 0.914 | 31.99 | 0.898 | 31.15 | 0.912 | 37.85 | 0.975 | |
Bicubic | 4× | 28.42 | 0.810 | 26.10 | 0.702 | 25.96 | 0.667 | 23.15 | 0.657 | 24.92 | 0.789 |
SRCNN[ | 30.49 | 0.862 | 27.61 | 0.751 | 26.91 | 0.710 | 24.53 | 0.722 | 27.66 | 0.858 | |
FSRCNN[ | 30.71 | 0.865 | 27.70 | 0.756 | 26.97 | 0.714 | 24.61 | 0.727 | 27.89 | 0.859 | |
VDSR[ | 31.53 | 0.883 | 28.03 | 0.767 | 27.29 | 0.725 | 25.18 | 0.752 | 28.82 | 0.886 | |
SRGAN[ | 29.46 | 0.838 | 26.60 | 0.718 | 25.74 | 0.666 | 24.50 | 0.736 | 27.79 | 0.856 | |
LapSRN[ | 31.54 | 0.885 | 28.09 | 0.770 | 27.31 | 0.727 | 25.21 | 0.756 | 29.09 | 0.890 | |
ADSR[ | 31.19 | 0.881 | 27.88 | 0.763 | 27.20 | 0.721 | 25.00 | 0.744 | — | — | |
DBPN[ | 31.76 | 0.887 | 28.39 | 0.778 | 27.48 | 0.733 | 25.71 | 0.772 | 30.22 | 0.902 | |
IFANet (本文方法) | 32.09 | 0.890 | 28.30 | 0.776 | 27.75 | 0.738 | 25.90 | 0.786 | 30.65 | 0.912 | |
Bicubic | 8× | 24.39 | 0.657 | 23.19 | 0.568 | 23.67 | 0.547 | 20.74 | 0.515 | 21.68 | 0.649 |
SRCNN[ | 25.33 | 0.689 | 23.85 | 0.593 | 24.13 | 0.565 | 21.29 | 0.543 | 22.37 | 0.682 | |
FSRCNN[ | 25.41 | 0.682 | 23.93 | 0.592 | 24.21 | 0.567 | 21.32 | 0.537 | 22.39 | 0.672 | |
VDSR[ | 25.72 | 0.711 | 24.21 | 0.609 | 24.37 | 0.576 | 21.54 | 0.560 | 22.83 | 0.707 | |
SRGAN[ | 23.04 | 0.626 | 21.57 | 0.495 | 21.78 | 0.442 | 19.64 | 0.468 | 20.42 | 0.625 | |
LapSRN[ | 26.15 | 0.737 | 24.35 | 0.620 | 24.54 | 0.585 | 21.81 | 0.580 | 23.39 | 0.734 | |
ADSR[ | 25.60 | 0.710 | 24.18 | 0.600 | 24.31 | 0.572 | 21.40 | 0.552 | 22.75 | 0.698 | |
DBPN[ | 26.43 | 0.748 | 24.39 | 0.623 | 24.60 | 0.589 | 22.01 | 0.592 | 23.97 | 0.756 | |
IFANet (本文方法) | 26.90 | 0.770 | 24.70 | 0.635 | 24.70 | 0.590 | 22.25 | 0.605 | 24.69 | 0.779 |
Tab. 3 Mean values of PSNR/SSIM of different algorithms on test sets under different amplification factors
算法 | 放大 倍数 | Set5 | Set14 | BSD100 | Urban100 | Manga109 | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | ||
Bicubic | 2× | 33.65 | 0.930 | 30.24 | 0.869 | 29.56 | 0.844 | 26.88 | 0.841 | 30.84 | 0.935 |
SRCNN[ | 36.66 | 0.954 | 32.45 | 0.906 | 31.36 | 0.888 | 29.52 | 0.895 | 35.72 | 0.968 | |
FSRCNN[ | 37.00 | 0.956 | 32.63 | 0.909 | 31.50 | 0.891 | 29.88 | 0.902 | — | — | |
VDSR[ | 37.74 | 0.959 | 32.97 | 0.913 | 31.90 | 0.896 | 30.77 | 0.914 | 37.16 | 0.974 | |
LapSRN[ | 37.52 | 0.929 | 33.08 | 0.913 | 31.80 | 0.895 | 31.05 | 0.910 | 37.53 | 0.974 | |
ADSR[ | 37.36 | 0.958 | 32.86 | 0.911 | 31.78 | 0.894 | 30.44 | 0.910 | — | — | |
DBPN[ | 37.60 | 0.959 | 33.18 | 0.914 | 31.94 | 0.897 | 31.20 | 0.918 | 37.57 | 0.974 | |
IFANet(本文方法) | 37.70 | 0.959 | 33.24 | 0.914 | 31.99 | 0.898 | 31.15 | 0.912 | 37.85 | 0.975 | |
Bicubic | 4× | 28.42 | 0.810 | 26.10 | 0.702 | 25.96 | 0.667 | 23.15 | 0.657 | 24.92 | 0.789 |
SRCNN[ | 30.49 | 0.862 | 27.61 | 0.751 | 26.91 | 0.710 | 24.53 | 0.722 | 27.66 | 0.858 | |
FSRCNN[ | 30.71 | 0.865 | 27.70 | 0.756 | 26.97 | 0.714 | 24.61 | 0.727 | 27.89 | 0.859 | |
VDSR[ | 31.53 | 0.883 | 28.03 | 0.767 | 27.29 | 0.725 | 25.18 | 0.752 | 28.82 | 0.886 | |
SRGAN[ | 29.46 | 0.838 | 26.60 | 0.718 | 25.74 | 0.666 | 24.50 | 0.736 | 27.79 | 0.856 | |
LapSRN[ | 31.54 | 0.885 | 28.09 | 0.770 | 27.31 | 0.727 | 25.21 | 0.756 | 29.09 | 0.890 | |
ADSR[ | 31.19 | 0.881 | 27.88 | 0.763 | 27.20 | 0.721 | 25.00 | 0.744 | — | — | |
DBPN[ | 31.76 | 0.887 | 28.39 | 0.778 | 27.48 | 0.733 | 25.71 | 0.772 | 30.22 | 0.902 | |
IFANet (本文方法) | 32.09 | 0.890 | 28.30 | 0.776 | 27.75 | 0.738 | 25.90 | 0.786 | 30.65 | 0.912 | |
Bicubic | 8× | 24.39 | 0.657 | 23.19 | 0.568 | 23.67 | 0.547 | 20.74 | 0.515 | 21.68 | 0.649 |
SRCNN[ | 25.33 | 0.689 | 23.85 | 0.593 | 24.13 | 0.565 | 21.29 | 0.543 | 22.37 | 0.682 | |
FSRCNN[ | 25.41 | 0.682 | 23.93 | 0.592 | 24.21 | 0.567 | 21.32 | 0.537 | 22.39 | 0.672 | |
VDSR[ | 25.72 | 0.711 | 24.21 | 0.609 | 24.37 | 0.576 | 21.54 | 0.560 | 22.83 | 0.707 | |
SRGAN[ | 23.04 | 0.626 | 21.57 | 0.495 | 21.78 | 0.442 | 19.64 | 0.468 | 20.42 | 0.625 | |
LapSRN[ | 26.15 | 0.737 | 24.35 | 0.620 | 24.54 | 0.585 | 21.81 | 0.580 | 23.39 | 0.734 | |
ADSR[ | 25.60 | 0.710 | 24.18 | 0.600 | 24.31 | 0.572 | 21.40 | 0.552 | 22.75 | 0.698 | |
DBPN[ | 26.43 | 0.748 | 24.39 | 0.623 | 24.60 | 0.589 | 22.01 | 0.592 | 23.97 | 0.756 | |
IFANet (本文方法) | 26.90 | 0.770 | 24.70 | 0.635 | 24.70 | 0.590 | 22.25 | 0.605 | 24.69 | 0.779 |
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