Underwater image restoration using Joint Local-Global Polarization Complementary Network

被引:0
作者
Ruan, Rui [1 ]
Zhang, Weidong [2 ]
Liang, Zheng [1 ]
机构
[1] Anhui Univ, Sch Internet, Hefei 230039, Peoples R China
[2] Henan Inst Sci & Technol, Sch Informat Engn, Xinxiang, Peoples R China
关键词
Polarization imaging; Deep learning; Image recovery; Generative adversarial networks; QUALITY ASSESSMENT; ENHANCEMENT;
D O I
10.1016/j.imavis.2025.105546
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Underwater image always suffers from the degradation of visual quality and lack of clear details caused by light scattering effect. Since polarization imaging can effectively eliminate the backscattering light, polarization-based methods become more attractive to restore the image, which utilize the difference of polarization characteristics to boost the restoration performance. In this paper, we propose an underwater image restoration using joint Local-Global Polarization Complementary Network, named LGPCNet, to achieve a clear underwater image from multi-polarization images. In particular, we design a local polarization complement module (LCM) to adaptively fuse complementary information of local regions from images with different polarization states. By incorporating this, we can restore rich details including color and texture from other polarimetric images. Then, to balance visual effects between images with different polarization states, we propose a global appearance sharing module (GSM) to obtain the consistent brightness across different polarization images. Finally, we adaptively aggregate the restored information from each polarization states to obtain a final clear image. Experiments on an extended natural underwater polarization image dataset demonstrate that our proposed method achieves superior image restoration performance in terms of color, brightness and contrast compared with state-of-the-art image restored methods.
引用
收藏
页数:10
相关论文
共 52 条
[1]   Multi-polarization fusion generative adversarial networks for clear underwater imaging [J].
Ding, Xueyan ;
Wang, Yafei ;
Fu, Xianping .
OPTICS AND LASERS IN ENGINEERING, 2022, 152
[2]  
Ding Xueyan, 2021, IEEE Geosci. Remote. Sens. Lett., V19, P1
[3]   Transmission Estimation in Underwater Single Images [J].
Drews-, P., Jr. ;
do Nascimento, E. ;
Moraes, F. ;
Botelho, S. ;
Campos, M. .
2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), 2013, :825-830
[4]   Underwater Depth Estimation and Image Restoration Based on Single Images [J].
Drews, Paulo L. J., Jr. ;
Nascimento, Erickson R. ;
Botelho, Silvia S. C. ;
Montenegro Campos, Mario Fernando .
IEEE COMPUTER GRAPHICS AND APPLICATIONS, 2016, 36 (02) :24-35
[5]   Image descattering and absorption compensation in underwater polarimetric imaging [J].
Fu, Xianping ;
Liang, Zheng ;
Ding, Xueyan ;
Yu, Xinyue ;
Wang, Yafei .
OPTICS AND LASERS IN ENGINEERING, 2020, 132
[6]  
Fu XY, 2017, I S INTELL SIG PROC, P789, DOI 10.1109/ISPACS.2017.8266583
[7]  
Fu XY, 2014, IEEE IMAGE PROC, P4572, DOI 10.1109/ICIP.2014.7025927
[8]   AutoGAN: Neural Architecture Search for Generative Adversarial Networks [J].
Gong, Xinyu ;
Chang, Shiyu ;
Jiang, Yifan ;
Wang, Zhangyang .
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, :3223-3233
[9]  
Guo Y., 2023, IEEE CVF C COMP VIS, P1884
[10]   Underwater Image Recovery Under the Nonuniform Optical Field Based on Polarimetric Imaging [J].
Hu, Haofeng ;
Zhao, Lin ;
Li, Xiaobo ;
Wang, Hui ;
Liu, Tiegen .
IEEE PHOTONICS JOURNAL, 2018, 10 (01)