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
来源
PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019) | 2019年
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
收藏
页码:866 / 870
页数:5
相关论文
共 50 条
  • [21] Efficient Attentional Underwater Image Enhancement Generative Adversarial Network
    Zhang, Haopeng
    Xu, Hongli
    Yu, Xiaosheng
    Wang, Junxiang
    Wu, Chengdong
    PROCEEDINGS OF THE 36TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC 2024, 2024, : 3813 - 3818
  • [22] Underwater image enhancement using a mixed generative adversarial network
    Mu, Delang
    Li, Heng
    Liu, Hui
    Dong, Ling
    Zhang, Guoyin
    IET IMAGE PROCESSING, 2023, 17 (04) : 1149 - 1160
  • [23] Physics-Based Generative Adversarial Models for Image Restoration and Beyond
    Pan, Jinshan
    Dong, Jiangxin
    Liu, Yang
    Zhang, Jiawei
    Ren, Jimmy
    Tang, Jinhui
    Tai, Yu-Wing
    Yang, Ming-Hsuan
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (07) : 2449 - 2462
  • [24] Window-based transformer generative adversarial network for autonomous underwater image enhancement
    Ummar, Mehnaz
    Dharejo, Fayaz Ali
    Alawode, Basit
    Mahbub, Taslim
    Piran, Md. Jalil
    Javed, Sajid
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 126
  • [25] 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)
  • [26] 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
  • [27] Large-area damage image restoration algorithm based on generative adversarial network
    Liu, Gang
    Li, Xiaofeng
    Wei, Jin
    NEURAL COMPUTING & APPLICATIONS, 2021, 33 (10) : 4651 - 4661
  • [28] Generative Adversarial Network-Based Restoration of Speckled SAR Images
    Wang, Puyang
    Zhang, He
    Patel, Vishal M.
    2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP), 2017,
  • [29] Underwater Attentional Generative Adversarial Networks for Image Enhancement
    Wang, Ning
    Chen, Tingkai
    Kong, Xiangjun
    Chen, Yanzheng
    Wang, Rongfeng
    Gong, Yongjun
    Song, Shiji
    IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2023, 53 (03) : 490 - 500
  • [30] 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)