Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (7): 2288-2294.DOI: 10.11772/j.issn.1001-9081.2022060840
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Anyang LIU1, Huaici ZHAO2, Wenlong CAI1(), Zechao XU2, Ruideng XIE1
Received:
2022-06-10
Revised:
2022-08-24
Accepted:
2022-08-26
Online:
2022-09-22
Published:
2023-07-10
Contact:
Wenlong CAI
About author:
LIU Anyang, born in 1997, M. S. candidate. His research interests include artificial intelligence, image processing, pattern recognition.Supported by:
刘安阳1, 赵怀慈2, 蔡文龙1(), 许泽超2, 解瑞灯1
通讯作者:
蔡文龙
作者简介:
刘安阳(1997—),男,山东济南人,硕士研究生,主要研究方向:人工智能、图像处理、模式识别;基金资助:
CLC Number:
Anyang LIU, Huaici ZHAO, Wenlong CAI, Zechao XU, Ruideng XIE. Adaptive image deblurring generative adversarial network algorithm based on active discrimination mechanism[J]. Journal of Computer Applications, 2023, 43(7): 2288-2294.
刘安阳, 赵怀慈, 蔡文龙, 许泽超, 解瑞灯. 基于主动判别机制的自适应生成对抗网络图像去模糊算法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2288-2294.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060840
算法 | GoPro性能测试 | Kohler性能测试 | ||||||
---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | VIF | AG | PSNR/dB | SSIM | VIF | AG | |
Nah算法[ | 28.834 | 0.851 | 0.81 | 2.84 | 26.428 | 0.731 | 0.73 | 2.57 |
DeblurGAN算法[ | 28.707 | 0.885 | 0.77 | 3.56 | 26.363 | 0.752 | 0.62 | 3.14 |
DeblurGANv2算法[ | 29.061 | 0.889 | 0.85 | 4.03 | 27.272 | 0.788 | 0.73 | 3.63 |
SRN算法[ | 29.115 | 0.883 | 0.42 | 4.51 | 27.554 | 0.794 | 0.34 | 4.32 |
MT-RNN算法[ | 29.633 | 0.905 | 0.85 | 3.97 | 28.476 | 0.822 | 0.75 | 3.76 |
本文算法 | 29.877 | 0.921 | 0.89 | 5.21 | 28.492 | 0.824 | 0.80 | 4.95 |
Tab. 1 Experimental results of objective comparison
算法 | GoPro性能测试 | Kohler性能测试 | ||||||
---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | VIF | AG | PSNR/dB | SSIM | VIF | AG | |
Nah算法[ | 28.834 | 0.851 | 0.81 | 2.84 | 26.428 | 0.731 | 0.73 | 2.57 |
DeblurGAN算法[ | 28.707 | 0.885 | 0.77 | 3.56 | 26.363 | 0.752 | 0.62 | 3.14 |
DeblurGANv2算法[ | 29.061 | 0.889 | 0.85 | 4.03 | 27.272 | 0.788 | 0.73 | 3.63 |
SRN算法[ | 29.115 | 0.883 | 0.42 | 4.51 | 27.554 | 0.794 | 0.34 | 4.32 |
MT-RNN算法[ | 29.633 | 0.905 | 0.85 | 3.97 | 28.476 | 0.822 | 0.75 | 3.76 |
本文算法 | 29.877 | 0.921 | 0.89 | 5.21 | 28.492 | 0.824 | 0.80 | 4.95 |
算法 | 单幅图像处理时间 | 视频数据处理时间 |
---|---|---|
Nah算法[ | 3.02 | 1 817 |
DeblurGAN算法[ | 0.91 | 547 |
SRN算法[ | 1.24 | 765 |
MT-RNN算法[ | 0.32 | 192 |
本文算法 | 0.54 | 108 |
Tab. 2 Test results of average running time
算法 | 单幅图像处理时间 | 视频数据处理时间 |
---|---|---|
Nah算法[ | 3.02 | 1 817 |
DeblurGAN算法[ | 0.91 | 547 |
SRN算法[ | 1.24 | 765 |
MT-RNN算法[ | 0.32 | 192 |
本文算法 | 0.54 | 108 |
算法结构 | PSNR/dB | SSIM | AG |
---|---|---|---|
Base | 27.981 | 0.822 | 3.02 |
Base+FPN | 28.522 | 0.852 | 3.11 |
Base+FPN+RAB | 29.523 | 0.877 | 3.31 |
Base+FPN+RAB+Loss | 29.401 | 0.904 | 4.63 |
Base+FPN+RAB+Double Discriminator | 29.877 | 0.921 | 5.21 |
Tab. 3 Ablation experimental results
算法结构 | PSNR/dB | SSIM | AG |
---|---|---|---|
Base | 27.981 | 0.822 | 3.02 |
Base+FPN | 28.522 | 0.852 | 3.11 |
Base+FPN+RAB | 29.523 | 0.877 | 3.31 |
Base+FPN+RAB+Loss | 29.401 | 0.904 | 4.63 |
Base+FPN+RAB+Double Discriminator | 29.877 | 0.921 | 5.21 |
Recall和AP指标变化/% | 样本数 | 对照评分 | |
---|---|---|---|
GoPro数据集 | 自制数据集 | ||
降低(0,5) | 44 | 21 | 0.864 |
提升(0,5) | 852 | 224 | 0.722 |
提升[5,10) | 755 | 235 | 0.632 |
提升10及以上 | 349 | 120 | 0.411 |
Tab. 4 Experimental results of object detection
Recall和AP指标变化/% | 样本数 | 对照评分 | |
---|---|---|---|
GoPro数据集 | 自制数据集 | ||
降低(0,5) | 44 | 21 | 0.864 |
提升(0,5) | 852 | 224 | 0.722 |
提升[5,10) | 755 | 235 | 0.632 |
提升10及以上 | 349 | 120 | 0.411 |
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