Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (8): 2578-2585.DOI: 10.11772/j.issn.1001-9081.2021061072
• Multimedia computing and computer simulation • Previous Articles
Chengxia XU1, Qing YAN1, Teng LI1,2, Kaichao MIAO3()
Received:
2021-06-24
Revised:
2022-01-01
Accepted:
2022-01-20
Online:
2022-03-02
Published:
2022-08-10
Contact:
Kaichao MIAO
About author:
XU Chengxia, born in 1995, M. S. candidate. Her research interests include image denoising based on deep learning.通讯作者:
苗开超
作者简介:
徐成霞(1995—),女,安徽合肥人,硕士研究生,主要研究方向:基于深度学习的图像去噪;CLC Number:
Chengxia XU, Qing YAN, Teng LI, Kaichao MIAO. De-raining algorithm based on joint attention mechanism for single image[J]. Journal of Computer Applications, 2022, 42(8): 2578-2585.
徐成霞, 阎庆, 李腾, 苗开超. 基于联合注意力机制的单幅图像去雨算法[J]. 《计算机应用》唯一官方网站, 2022, 42(8): 2578-2585.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021061072
算法 | SSIM/VIFP/PSNR | ||
---|---|---|---|
Rain100H | DID-MDN | SPANet-Data | |
原始雨图 | 0.357/0.172/12.13 | 0.706/0.363/22.16 | 0.904/0.753/32.63 |
PReNet[ | 0.858/0.679/26.77 | 0.945/0.861/32.44 | 0.937/0.810/35.60 |
SPANet[ | 0.847/0.702/27.34 | 0.947/0.847/32.06 | 0.975/0.826/36.83 |
Syn2Real[ | 0.860/0.715/28.77 | 0.947/0.861/34.33 | 0.962/0.817/36.79 |
本文算法 | 0.856/0.715/27.20 | 0.951/0.859/32.15 | 0.975/0.838/36.97 |
Tab. 1 Comparison of results of different de-raining algorithms
算法 | SSIM/VIFP/PSNR | ||
---|---|---|---|
Rain100H | DID-MDN | SPANet-Data | |
原始雨图 | 0.357/0.172/12.13 | 0.706/0.363/22.16 | 0.904/0.753/32.63 |
PReNet[ | 0.858/0.679/26.77 | 0.945/0.861/32.44 | 0.937/0.810/35.60 |
SPANet[ | 0.847/0.702/27.34 | 0.947/0.847/32.06 | 0.975/0.826/36.83 |
Syn2Real[ | 0.860/0.715/28.77 | 0.947/0.861/34.33 | 0.962/0.817/36.79 |
本文算法 | 0.856/0.715/27.20 | 0.951/0.859/32.15 | 0.975/0.838/36.97 |
测试数据集 | SSIM/VIFP/PSNR | |||
---|---|---|---|---|
原始雨图 | Rain100H&100L | DID-MDN | MSR-Synthetic | |
Rain100H | 0.357/0.172/13.13 | 0.917/0.863/29.15 | 0.731/0.761/24.71 | 0.786/0.764/26.85 |
Rain100L | 0.713/0.402/23.64 | 0.921/0.865/29.34 | 0.911/0.806/27.61 | 0.913/0.807/29.37 |
DID-MDN | 0.706/0.363/22.16 | 0.844/0.841/26.33 | 0.947/0.860/33.87 | 0.958/0.860/34.25 |
MSR-Synthetic | 0.762/0.397/23.07 | 0.908/0.843/26.97 | 0.944/0.861/33.63 | 0.960/0.861/36.65 |
Tab. 2 Cross-validation results on synthetic rain data
测试数据集 | SSIM/VIFP/PSNR | |||
---|---|---|---|---|
原始雨图 | Rain100H&100L | DID-MDN | MSR-Synthetic | |
Rain100H | 0.357/0.172/13.13 | 0.917/0.863/29.15 | 0.731/0.761/24.71 | 0.786/0.764/26.85 |
Rain100L | 0.713/0.402/23.64 | 0.921/0.865/29.34 | 0.911/0.806/27.61 | 0.913/0.807/29.37 |
DID-MDN | 0.706/0.363/22.16 | 0.844/0.841/26.33 | 0.947/0.860/33.87 | 0.958/0.860/34.25 |
MSR-Synthetic | 0.762/0.397/23.07 | 0.908/0.843/26.97 | 0.944/0.861/33.63 | 0.960/0.861/36.65 |
测试数据集 | SSIM/VIFP/PSNR | ||
---|---|---|---|
原图 | SPANet-Data | MSR-Real | |
SPANet-Data | 0.904/0.753/32.63 | 0.986/0.878/38.06 | 0.985/0.875/37.87 |
MSR-Real | 0.910/0.802/32.83 | 0.985/0.880/37.81 | 0.988/0.881/38.90 |
Tab. 3 Cross-validation results on real rain data
测试数据集 | SSIM/VIFP/PSNR | ||
---|---|---|---|
原图 | SPANet-Data | MSR-Real | |
SPANet-Data | 0.904/0.753/32.63 | 0.986/0.878/38.06 | 0.985/0.875/37.87 |
MSR-Real | 0.910/0.802/32.83 | 0.985/0.880/37.81 | 0.988/0.881/38.90 |
模块 | PSNR/dB | SSIM | 运行时间/s | |
---|---|---|---|---|
512×512 | 1 024×1 024 | |||
37.23 | 0.985 | 0.103 | 0.341 | |
37.43 | 0.985 | 0.126 | 0.423 | |
37.31 | 0.984 | 0.117 | 0.351 | |
38.90 | 0.988 | 0.132 | 0.441 |
Tab. 4 Network module analysis
模块 | PSNR/dB | SSIM | 运行时间/s | |
---|---|---|---|---|
512×512 | 1 024×1 024 | |||
37.23 | 0.985 | 0.103 | 0.341 | |
37.43 | 0.985 | 0.126 | 0.423 | |
37.31 | 0.984 | 0.117 | 0.351 | |
38.90 | 0.988 | 0.132 | 0.441 |
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