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基于UNDERWATER-CUT模型的水下图像增强算法

姚鹏 刘玉会

姚鹏, 刘玉会. 基于UNDERWATER-CUT模型的水下图像增强算法[J]. 水下无人系统学报, 2022, 30(5): 605-611 doi: 10.11993/j.issn.2096-3920.202111004
引用本文: 姚鹏, 刘玉会. 基于UNDERWATER-CUT模型的水下图像增强算法[J]. 水下无人系统学报, 2022, 30(5): 605-611 doi: 10.11993/j.issn.2096-3920.202111004
YAO Peng, LIU Yu-hui. Underwater Image Enhancement Based on UNDERWATER-CUT Model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611. doi: 10.11993/j.issn.2096-3920.202111004
Citation: YAO Peng, LIU Yu-hui. Underwater Image Enhancement Based on UNDERWATER-CUT Model[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 605-611. doi: 10.11993/j.issn.2096-3920.202111004

基于UNDERWATER-CUT模型的水下图像增强算法

doi: 10.11993/j.issn.2096-3920.202111004
基金项目: 国家自然科学基金项目资助(51909252).
详细信息
    作者简介:

    姚鹏:姚 鹏(1989-), 男, 博士, 副教授, 主要研究方向为无人系统智能规划与自主控制

  • 中图分类号: U675.81; TJ630

Underwater Image Enhancement Based on UNDERWATER-CUT Model

  • 摘要: 针对水下图像的颜色失真和对比度失衡问题, 提出了一种基于UNDERWATER-CUT模型的弱监督水下图像增强算法。该算法网络训练时无需成对训练集, 通过图像分块的方式构建对比学习正负样本, 约束了图像生成的内容。使用了结构相似性损失函数对水下图像增强进行约束, 确保CUT模型在对水下图像域到脱水图像域进行转换过程中的物体结构不变。同时还使用alignment和uniformity 2个简单函数的线性组合来近似逼近CUT模型的InfoNCE损失函数, 使得改进后的模型训练更容易收敛到最优值。实验结果证明经过文中算法增强后的图像, 颜色失真得到极大的修正, 图像中的物体结构和修复前的图像基本一致。

     

  • 图  1  UNDERWATER-CUT模型网络结构

    Figure  1.  Network structure of UNDERWTAER-CUT model

    图  2  生成器模型网络结构

    Figure  2.  Network structure of generator model

    图  3  判别器模型网络结构

    Figure  3.  Network structure of discriminator model

    图  4  不同方法在相同数据集下的增强效果

    Figure  4.  Enhanced results of different methods in the same dataset

    表  1  各方法增强后图像指标对比

    Table  1.   Comparison of the image indicators enhanced by each method

    模型FIDUCIQEPSNR
    CycleGAN129.630.551 914.99
    SSIM-CycleGAN107.770.557 814.85
    CUT182.380.551 613.48
    SSIM-CUT113.660.625 218.62
    UNDERWATER-CUT104.530.634 218.18
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-16
  • 录用日期:  2022-08-12
  • 修回日期:  2022-01-21
  • 网络出版日期:  2022-09-05

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