Fast processing of underwater active polarimetric dehazing imaging without prior knowledge

被引:2
作者
Xiang, Yanfa [1 ]
Wang, Guochen [1 ]
Gao, Jie [1 ]
Wang, Xin [1 ]
Chen, Yubin [1 ]
Chew, Khian-Hooi [1 ]
Chen, Rui-Pin [1 ]
机构
[1] Zhejiang Sci Tech Univ, Key Lab Opt Field Manipulat Zhejiang Prov, Dept Phys, Hangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
polarimetric dehazing imaging; degree of linear polarization; fast underwater dehazing; QUALITY ASSESSMENT; POLARIZATION; VISIBILITY;
D O I
10.1117/1.JEI.32.3.033026
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The scattering and absorption of suspended particles in water severely restrict the quality of underwater imaging. Several works have been proposed to improve the dehazing effect, but their efficiency was not considered. However, the processing efficiency of dehazing is an important indicator of the computing cost in an active application. An improved dehazing method that analyzes and optimizes the estimation accuracy of polarization parameters in the traditional underwater polarization imaging model is proposed. The degree of linear polarization of the target object light and the backscattered light and the transmission coefficient in the scattering medium are jointly estimated by the stochastic gradient descent method. The experimental results indicate that the proposed method can better preserve image details at a faster speed than the traditional underwater dehazing imaging method, especially in high-turbidity water environments. The average speed of the algorithm for restoring a 1024 x 768 pixel image is about 6.2 ms. (C) 2023 SPIE and IS&T
引用
收藏
页数:8
相关论文
共 19 条
  • [1] BACKPROPAGATION AND STOCHASTIC GRADIENT DESCENT METHOD
    AMARI, S
    [J]. NEUROCOMPUTING, 1993, 5 (4-5) : 185 - 196
  • [2] Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
    Bosse, Sebastian
    Maniry, Dominique
    Mueller, Klaus-Robert
    Wiegand, Thomas
    Samek, Wojciech
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 27 (01) : 206 - 219
  • [3] Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
    Das, Sourya Dipta
    Dutta, Saikat
    [J]. 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2020), 2020, : 1994 - 2001
  • [4] He JX, 2019, Arxiv, DOI arXiv:1904.08573
  • [5] Single Image Haze Removal Using Dark Channel Prior
    He, Kaiming
    Sun, Jian
    Tang, Xiaoou
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2011, 33 (12) : 2341 - 2353
  • [6] Depolarization index from Mueller matrix descatters imaging in turbid water
    Liu, Fei
    Zhang, Shichao
    Han, Pingli
    Chen, Fangyi
    Zhao, Lin
    Fan, Yingying
    Shao, Xiaopeng
    [J]. CHINESE OPTICS LETTERS, 2022, 20 (02)
  • [7] Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene feature
    Qu, Yufu
    Zou, Zhaofan
    [J]. OPTICS EXPRESS, 2017, 25 (21): : 25004 - 25022
  • [8] The underwater polarization dehazing imaging with a lightweight convolutional neural network
    Ren, Qiming
    Xiang, Yanfa
    Wang, Guochen
    Gao, Jie
    Wu, Yan
    Chen, Rui-Pin
    [J]. OPTIK, 2022, 251
  • [9] Recovery of underwater visibility and structure by polarization analysis
    Schechner, YY
    Karpel, N
    [J]. IEEE JOURNAL OF OCEANIC ENGINEERING, 2005, 30 (03) : 570 - 587
  • [10] PSNR vs SSIM: imperceptibility quality assessment for image steganography
    Setiadi, De Rosal Igantius Moses
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (06) : 8423 - 8444