Restoration of Underwater Distorted Image Sequence Based on Generative Adversarial Network

被引:0
|
作者
He, Changxin [1 ]
Zhang, Zhen [1 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai, Peoples R China
关键词
image restoration; distorted image; turbulence; generative adversarial network;
D O I
10.1109/itaic.2019.8785496
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Underwater images will be distorted due to the influence of turbulence, and images will appear geometric distortion since the light is refracted by the turbulence, which makes task of image recognition difficult. In order to improve image recognition underwater, this paper proposes an image restoration method using underwater distorted image sequence through deep learning technique. Considering the complexity of dynamics motion, image sequence is more feasible to realize task of restoration, which contains enough information of water turbulence. Generative adversarial network as a deep neural network has proved to be an appropriate method applying to the field of image processing, which is used to restore the distorted image. Experiment shows the proposed method has fine ability of using distorted image sequence to realize image restoration.
引用
下载
收藏
页码:866 / 870
页数:5
相关论文
共 50 条
  • [41] Blind restoration of astronomical image based on deep attention generative adversarial neural network
    Luo, Lin
    Bao, Jiaqi
    Li, Jinlong
    Gao, Xiaorong
    OPTICAL ENGINEERING, 2022, 61 (01)
  • [42] Large-area damage image restoration algorithm based on generative adversarial network
    Gang Liu
    Xiaofeng Li
    Jin Wei
    Neural Computing and Applications, 2021, 33 : 4651 - 4661
  • [43] Large-area damage image restoration algorithm based on generative adversarial network
    Liu, Gang
    Li, Xiaofeng
    Wei, Jin
    Neural Computing and Applications, 2021, 33 (10) : 4651 - 4661
  • [44] Image restoration of motion artifacts in cardiac arteries and vessels based on a generative adversarial network
    Deng, Fuquan
    Wan, Qian
    Zeng, Yingting
    Shi, Yanbin
    Wu, Huiying
    Wu, Yu
    Xu, Weifeng
    Mok, Greta S. P.
    Zhang, Xiaochun
    Hu, Zhanli
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2022, 12 (05) : 2755 - 2766
  • [45] An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index
    Hu, Kai
    Zhang, Yanwen
    Weng, Chenghang
    Wang, Pengsheng
    Deng, Zhiliang
    Liu, Yunping
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2021, 9 (07)
  • [46] Image Dehazing Based on Generative Adversarial Network
    Huang S.
    Wang B.
    Li H.
    Yang Y.
    Hu W.
    Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2021, 34 (11): : 990 - 1003
  • [47] Image Demosaicing Based on Generative Adversarial Network
    Luo, Jingrui
    Wang, Jie
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
  • [48] Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network
    Guo, Yecai
    Li, Hanyu
    Zhuang, Peixian
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2020, 45 (03) : 862 - 870
  • [49] TEGAN: Transformer Embedded Generative Adversarial Network for Underwater Image Enhancement
    Zhi Gao
    Jing Yang
    Lu Zhang
    Fengling Jiang
    Xixiang Jiao
    Cognitive Computation, 2024, 16 : 191 - 214
  • [50] TEGAN: Transformer Embedded Generative Adversarial Network for Underwater Image Enhancement
    Gao, Zhi
    Yang, Jing
    Zhang, Lu
    Jiang, Fengling
    Jiao, Xixiang
    COGNITIVE COMPUTATION, 2024, 16 (01) : 191 - 214