Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 567-574.DOI: 10.11772/j.issn.1001-9081.2021122091
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Li’an ZHU1,2(), Hong ZHANG1,2
Received:
2021-12-14
Revised:
2022-07-07
Accepted:
2022-07-18
Online:
2022-09-23
Published:
2023-02-10
Contact:
Li’an ZHU
About author:
ZHANG Hong, born in 1979, Ph. D., professor. Her research interests include cross-media retrieval, machine learning, data mining.
通讯作者:
朱利安
作者简介:
张鸿(1979—),女,湖北襄阳人,教授,博士,主要研究方向:跨媒体检索、机器学习、数据挖掘。
CLC Number:
Li’an ZHU, Hong ZHANG. Nonhomogeneous image dehazing based on dual-branch conditional generative adversarial network[J]. Journal of Computer Applications, 2023, 43(2): 567-574.
朱利安, 张鸿. 基于双分支条件生成对抗网络的非均匀图像去雾[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 567-574.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021122091
网络层 | 输出矩阵 | 参数个数 |
---|---|---|
ReflectionPad2d | 77×102×256 | 0 |
Conv2d | 75×100×256 | 590 080 |
PReLU | 75×100×256 | 1 |
ReflectionPad2d | 77×102×256 | 0 |
Conv2d | 75×100×256 | 590 080 |
ResidualBlock | 75×100×256 | 0 |
Tab. 1 Structure information of residual block
网络层 | 输出矩阵 | 参数个数 |
---|---|---|
ReflectionPad2d | 77×102×256 | 0 |
Conv2d | 75×100×256 | 590 080 |
PReLU | 75×100×256 | 1 |
ReflectionPad2d | 77×102×256 | 0 |
Conv2d | 75×100×256 | 590 080 |
ResidualBlock | 75×100×256 | 0 |
网络层 | 输出矩阵 | 参数个数 |
---|---|---|
Conv2d | 1 200×1 600×32 | 9 248 |
ReLU | 1 200×1 600×32 | 0 |
Conv2d | 1 200×1 600×32 | 9 248 |
AdaptiveAvgPool2d | 1×1×32 | 0 |
Conv2d | 1×1×4 | 132 |
ReLU | 1×1×4 | 0 |
Conv2d | 1×1×32 | 160 |
Sigmoid | 1×1×32 | 0 |
RCAB | 1 200×1 600×32 | 0 |
Tab. 2 Structure information of RCAB
网络层 | 输出矩阵 | 参数个数 |
---|---|---|
Conv2d | 1 200×1 600×32 | 9 248 |
ReLU | 1 200×1 600×32 | 0 |
Conv2d | 1 200×1 600×32 | 9 248 |
AdaptiveAvgPool2d | 1×1×32 | 0 |
Conv2d | 1×1×4 | 132 |
ReLU | 1×1×4 | 0 |
Conv2d | 1×1×32 | 160 |
Sigmoid | 1×1×32 | 0 |
RCAB | 1 200×1 600×32 | 0 |
方法 | NH-HAZE | NH-HAZE2 | 参数量/MB | 运行时间/s | ||
---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | |||
OUN | 17.32(100%) | 0.587(100%) | 17.99(100%) | 0.742(100%) | 29.69 | 0.018 |
EUN | 17.68(102%) | 0.611(104%) | 18.67(103%) | 0.771(103%) | 140.90 | 0.024 |
AN | 17.64(101%) | 0.625(106%) | 19.06(105%) | 0.785(105%) | 1.00 | 0.028 |
EUN+AN | 18.12(104%) | 0.639(108%) | 19.21(106%) | 0.788(106%) | 141.90 | 0.066 |
DB⁃CGAN | 18.26(105%) | 0.640(109%) | 19.33(107%) | 0.791(106%) | 141.90 | 0.069 |
Tab. 3 Comparison of different methods on NH-HAZE and NH-HAZE2 datasets
方法 | NH-HAZE | NH-HAZE2 | 参数量/MB | 运行时间/s | ||
---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | |||
OUN | 17.32(100%) | 0.587(100%) | 17.99(100%) | 0.742(100%) | 29.69 | 0.018 |
EUN | 17.68(102%) | 0.611(104%) | 18.67(103%) | 0.771(103%) | 140.90 | 0.024 |
AN | 17.64(101%) | 0.625(106%) | 19.06(105%) | 0.785(105%) | 1.00 | 0.028 |
EUN+AN | 18.12(104%) | 0.639(108%) | 19.21(106%) | 0.788(106%) | 141.90 | 0.066 |
DB⁃CGAN | 18.26(105%) | 0.640(109%) | 19.33(107%) | 0.791(106%) | 141.90 | 0.069 |
PSNR/dB | SSIM | |||||
---|---|---|---|---|---|---|
√ | 12.21 | 0.233 | ||||
√ | 19.12 | 0.771 | ||||
√ | √ | 19.19 | 0.782 | |||
√ | √ | √ | 19.28 | 0.783 | ||
√ | √ | √ | √ | 19.33 | 0.791 | |
√ | √ | √ | √ | 19.25 | 0.779 |
Tab. 4 Comparison of experimental results of loss function ablation
PSNR/dB | SSIM | |||||
---|---|---|---|---|---|---|
√ | 12.21 | 0.233 | ||||
√ | 19.12 | 0.771 | ||||
√ | √ | 19.19 | 0.782 | |||
√ | √ | √ | 19.28 | 0.783 | ||
√ | √ | √ | √ | 19.33 | 0.791 | |
√ | √ | √ | √ | 19.25 | 0.779 |
PSNR/dB | SSIM | |||
---|---|---|---|---|
0.001 | 0.2 | 0.005 | 19.33 | 0.791 |
0.010 | 0.2 | 0.050 | 19.31 | 0.784 |
Tab. 5 Experimental results of weight of mixed loss function
PSNR/dB | SSIM | |||
---|---|---|---|---|
0.001 | 0.2 | 0.005 | 19.33 | 0.791 |
0.010 | 0.2 | 0.050 | 19.31 | 0.784 |
方法 | NH-HAZE | NH-HAZE2 | 参数量/MB | 运行时间/s | ||
---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | |||
DCP[ | 12.01(100%) | 0.505(122%) | 11.78(100%) | 0.673(105%) | >1 | |
AODNet[ | 12.72(105%) | 0.413(100%) | 13.64(115%) | 0.635(100%) | 0.01 | 0.006 |
GCANet[ | 16.12(134%) | 0.579(140%) | 17.11(145%) | 0.763(120%) | 2.68 | 0.184 |
MSBDN[ | 18.27(152%) | 0.615(148%) | 18.67(158%) | 0.742(116%) | 140.60 | 0.058 |
MPSHAN[ | 18.13(150%) | 0.641(155%) | 18.97(161%) | 0.781(122%) | 109.80 | 0.042 |
本文方法 | 18.29(152%) | 0.633(153%) | 19.33(164%) | 0.791(124%) | 141.90 | 0.069 |
Tab. 6 Quantitative comparison of different methods on NH-HAZE and NH-HAZE2 datasets
方法 | NH-HAZE | NH-HAZE2 | 参数量/MB | 运行时间/s | ||
---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | |||
DCP[ | 12.01(100%) | 0.505(122%) | 11.78(100%) | 0.673(105%) | >1 | |
AODNet[ | 12.72(105%) | 0.413(100%) | 13.64(115%) | 0.635(100%) | 0.01 | 0.006 |
GCANet[ | 16.12(134%) | 0.579(140%) | 17.11(145%) | 0.763(120%) | 2.68 | 0.184 |
MSBDN[ | 18.27(152%) | 0.615(148%) | 18.67(158%) | 0.742(116%) | 140.60 | 0.058 |
MPSHAN[ | 18.13(150%) | 0.641(155%) | 18.97(161%) | 0.781(122%) | 109.80 | 0.042 |
本文方法 | 18.29(152%) | 0.633(153%) | 19.33(164%) | 0.791(124%) | 141.90 | 0.069 |
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