Underwater Mueller matrix de-scattering imaging under the influence of natural light

被引:0
|
作者
Li, Huihui [1 ,2 ,3 ]
Pardo, Iago [3 ,4 ]
Arteaga, Oriol [3 ,4 ]
机构
[1] Jimei Univ, Sch Marine Engn, Xiamen 361021, Peoples R China
[2] Jimei Univ, Marine Engn Coll, Key Lab Fujian Prov Marine & Ocean Engn, Xiamen 361021, Peoples R China
[3] Univ Barcelona, Dep Fis Aplicada, Barcelona 08028, Spain
[4] Univ Barcelona, Inst Nanosci & Nanotechnol IN2UB, Barcelona 08028, Spain
关键词
Polarimetric imaging; Underwater descattering; Mueller matrix; Active polarization illumination; Natural light; INPUT POLARIZATION STATES; DEPOLARIZATION; RECOVERY; TARGETS; VISION;
D O I
10.1016/j.optlaseng.2024.108804
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Underwater polarization imaging in real-world marine conditions is influenced by both active polarization illumination and unpolarized natural light, a factor often overlooked in existing research. This paper presents three polarization de-scattering approaches aimed at optimizing the image model, suppressing background light, and enhancing image contrast. We investigated the effectiveness of these methods under various scattering conditions, particularly focusing on the impact of natural light on active polarization illumination measurements. The experimental results indicate that the presence of natural light causes the degree of circular polarization to be slightly higher than the degree of linear polarization. Consequently, the same de-scattering effect can be achieved using either linearly polarized or circularly polarized detection within the image model-based approach. The method of suppressing background light is not effective when the background light is significantly uneven and has a small degree of polarization. Our proposed method for enhancing image contrast does not require estimating any parameters or variables about objects and background, making it adaptable to underwater images with non-uniform illumination. This research provides a foundation for selecting appropriate measurement and analysis schemes for underwater imaging in real-world environments.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] De-Scattering Algorithm for Underwater Mueller Matrix Images Based on Residual UNet
    Li Xiaohuan
    Wang Xia
    Wang Conghe
    Zhang Xin
    ACTA OPTICA SINICA, 2022, 42 (24)
  • [2] Underwater De-scattering Imaging by Laser Field Synchronous Scanning
    Wu, Houde
    Zhao, Ming
    Xu, Wenhai
    OPTICS AND LASERS IN ENGINEERING, 2020, 126
  • [3] Underwater polarization de-scattering imaging independent of target-free region
    Zhu Yeqing
    Wang Xing
    Zhu Zhuqing
    ACTA PHYSICA SINICA, 2025, 74 (04)
  • [4] De-scattering method of underwater image based on imaging of specific polarization state
    Chu J.-K.
    Zhang P.-Q.
    Cheng H.-Y.
    Zhang R.
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2021, 29 (05): : 1207 - 1215
  • [5] Underwater Imaging by Suppressing the Backscattered Light Based on Mueller Matrix
    Wang, Hongyuan
    Li, Jiaqi
    Hu, Haofeng
    Jiang, Junfeng
    Li, Xiaobo
    Zhao, Kan
    Cheng, Zhenzhou
    Sang, Mei
    Liu, Tiegen
    IEEE PHOTONICS JOURNAL, 2021, 13 (04):
  • [6] A Novel Approach to Underwater De-scattering Based on Sparse and Low-rank Matrix Decomposition
    Jiang, Qin
    Wang, Guoyu
    Gong, Benxing
    Tan, Yibo
    2018 OCEANS - MTS/IEEE KOBE TECHNO-OCEANS (OTO), 2018,
  • [7] A Novel Underwater De-scattering Method Based on Sparse Non-negative Matrix Factorization
    Liu, Xiaopeng
    Saeeda, Hina
    Dong, Junyu
    2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI), 2017,
  • [8] UNDERWATER IMAGE DE-SCATTERING AND ENHANCING USING DEHAZENET AND HWD
    Pan, Pan-wang
    Yuan, Fei
    Cheng, En
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN, 2018, 26 (04): : 531 - 540
  • [9] Underwater image de-scattering and classification by deep neural network
    Li, Yujie
    Lu, Huimin
    Li, Jianru
    Li, Xin
    Li, Yun
    Serikawa, Seiichi
    COMPUTERS & ELECTRICAL ENGINEERING, 2016, 54 : 68 - 77
  • [10] Non-uniform de-Scattering and de-Blurring of Underwater Images
    Yujie Li
    Huimin Lu
    Kuan-Ching Li
    Hyoungseop Kim
    Seiichi Serikawa
    Mobile Networks and Applications, 2018, 23 : 352 - 362