Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2175-2182.DOI: 10.11772/j.issn.1001-9081.2023070933
• Multimedia computing and computer simulation • Previous Articles Next Articles
Dahai LI, Zhonghua WANG(), Zhendong WANG
Received:
2023-07-13
Revised:
2023-09-16
Accepted:
2023-09-20
Online:
2023-10-26
Published:
2024-07-10
Contact:
Zhonghua WANG
About author:
LI Dahai, born in 1975, Ph. D., associate professor. His research interests include intelligent optimization algorithms, deep learning.Supported by:
通讯作者:
王忠华
作者简介:
李大海(1975—),男,山东乳山人,副教授,博士,CCF会员,主要研究方向:智能优化算法、深度学习;基金资助:
CLC Number:
Dahai LI, Zhonghua WANG, Zhendong WANG. Dual-branch low-light image enhancement network combining spatial and frequency domain information[J]. Journal of Computer Applications, 2024, 44(7): 2175-2182.
李大海, 王忠华, 王振东. 结合空间域和频域信息的双分支低光照图像增强网络[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2175-2182.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023070933
网络模型 | PSNR/dB | SSIM | LPIPS | 参数量/106 |
---|---|---|---|---|
Retinex-Net[ | 16.77 | 0.462 | 0.474 | 0.44 |
Zero-DCE[ | 14.86 | 0.589 | 0.335 | 0.08 |
DRBN[ | 15.13 | 0.472 | 0.316 | 0.58 |
EnlightenGAN[ | 17.48 | 0.677 | 0.322 | 8.64 |
KinD++[ | 20.86 | 0.760 | 0.164 | 8.27 |
IAT[ | 23.38 | 0.809 | 0.261 | 0.09 |
LLFormer[ | 23.64 | 0.816 | 0.169 | 24.52 |
SAFNet | 22.11 | 0.823 | 0.114 | 0.07 |
Tab. 1 Objective evaluation results of different network models on LOL dataset
网络模型 | PSNR/dB | SSIM | LPIPS | 参数量/106 |
---|---|---|---|---|
Retinex-Net[ | 16.77 | 0.462 | 0.474 | 0.44 |
Zero-DCE[ | 14.86 | 0.589 | 0.335 | 0.08 |
DRBN[ | 15.13 | 0.472 | 0.316 | 0.58 |
EnlightenGAN[ | 17.48 | 0.677 | 0.322 | 8.64 |
KinD++[ | 20.86 | 0.760 | 0.164 | 8.27 |
IAT[ | 23.38 | 0.809 | 0.261 | 0.09 |
LLFormer[ | 23.64 | 0.816 | 0.169 | 24.52 |
SAFNet | 22.11 | 0.823 | 0.114 | 0.07 |
网络模型 | PSNR/dB | SSIM |
---|---|---|
Retinex-Net[ | 15.90 | 0.373 |
Zero-DCE[ | 15.83 | 0.466 |
DRBN[ | 16.15 | 0.542 |
EnlightenGAN[ | 16.31 | 0.469 |
KinD++[ | 16.47 | 0.492 |
IAT[ | 16.51 | 0.516 |
LLFormer[ | 17.16 | 0.522 |
SAFNet | 17.23 | 0.550 |
Tab. 2 Objective evaluation results of different network models on LSRW dataset
网络模型 | PSNR/dB | SSIM |
---|---|---|
Retinex-Net[ | 15.90 | 0.373 |
Zero-DCE[ | 15.83 | 0.466 |
DRBN[ | 16.15 | 0.542 |
EnlightenGAN[ | 16.31 | 0.469 |
KinD++[ | 16.47 | 0.492 |
IAT[ | 16.51 | 0.516 |
LLFormer[ | 17.16 | 0.522 |
SAFNet | 17.23 | 0.550 |
模型 | PSNR/dB | SSIM |
---|---|---|
w/o FB | 21.62 | 0.814 |
w/o Fusion | 21.81 | 0.819 |
w/o ECA | 21.76 | 0.816 |
SAFNet | 22.11 | 0.823 |
Tab. 3 Objective evaluation results of different model structures on LOL dataset
模型 | PSNR/dB | SSIM |
---|---|---|
w/o FB | 21.62 | 0.814 |
w/o Fusion | 21.81 | 0.819 |
w/o ECA | 21.76 | 0.816 |
SAFNet | 22.11 | 0.823 |
损失函数 | PSNR/dB | SSIM |
---|---|---|
20.16 | 0.809 | |
21.74 | 0.817 | |
21.97 | 0.819 | |
22.11 | 0.823 |
Tab. 4 Objective evaluation results of different loss functions on LOL dataset
损失函数 | PSNR/dB | SSIM |
---|---|---|
20.16 | 0.809 | |
21.74 | 0.817 | |
21.97 | 0.819 | |
22.11 | 0.823 |
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