Laser & Optoelectronics Progress, Volume. 59, Issue 4, 0415007(2022)

Image Defogging Algorithm Based on Generative Adversarial Network

Weifeng Zhong1,2、* and Jing Zhao1
Author Affiliations
  • 1School of Automation, Harbin University of Science and Technology, Harbin , Heilongjiang 150080, China
  • 2Heilongjiang Key Laboratory of Complex Intelligent System and Integration, Harbin , Heilongjiang 150080, China
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    References(16)

    [1] Gao S N. Research on image defogging based on convolutional neural network[D](2021).

    [2] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative Adversarial Nets[C], 2672-2680(2014).

    [9] Wang D W, Li S L, Han P F et al. Feature constraint CycleGAN for single image dehazing algorithm[J]. Laser & Optoelectronics Progress, 58, 1410017(2021).

    [12] Liu Y H, Wu S. Image dehazing algorithm based on multi-scale fusion and adversarial training[J]. Laser & Optoelectronics Progress, 57, 061015(2020).

    [14] Zhang Q, Ye B, Luo S Q et al. Aluminum plate defect image segmentation using improved generative adversarial networks for eddy current detection[J]. Laser & Optoelectronics Progress, 58, 0815002(2021).

    [15] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C](2015).

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    Weifeng Zhong, Jing Zhao. Image Defogging Algorithm Based on Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0415007

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    Paper Information

    Category: Machine Vision

    Received: Jul. 14, 2021

    Accepted: Sep. 13, 2021

    Published Online: Feb. 15, 2022

    The Author Email: Weifeng Zhong (zhongweifeng@hrbust.edu.cn)

    DOI:10.3788/LOP202259.0415007

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