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散射光补偿结合色彩保持与对比度均衡的水下图像增强

宁泽萌 林森 李兴然

宁泽萌, 林森, 李兴然. 散射光补偿结合色彩保持与对比度均衡的水下图像增强[J]. 水下无人系统学报, 2024, 32(6): 1-10 doi: 10.11993/j.issn.2096-3920.2023-0131
引用本文: 宁泽萌, 林森, 李兴然. 散射光补偿结合色彩保持与对比度均衡的水下图像增强[J]. 水下无人系统学报, 2024, 32(6): 1-10 doi: 10.11993/j.issn.2096-3920.2023-0131
NING Zemeng, LIN Sen, LI Xingran. Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0131
Citation: NING Zemeng, LIN Sen, LI Xingran. Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0131

散射光补偿结合色彩保持与对比度均衡的水下图像增强

doi: 10.11993/j.issn.2096-3920.2023-0131
基金项目: 国家重点研发计划项目(2018YFB1403303), 辽宁省教育厅高等学校基本科研项目(LJKMZ20220615).
详细信息
    通讯作者:

    林 森(1980-), 男, 博士, 副教授, 主要研究方向为机器视觉、深度学习、模式识别

  • 中图分类号: TJ630

Scattered Light Compensation Combined with Color Preservation and Contrast Balance for Underwater Image Enhancement

  • 摘要: 针对水下图像存在颜色偏差、对比度低及模糊等问题, 提出散射光补偿结合色彩保持与对比度均衡的水下图像增强方法。首先, 利用相对总变分模型分离图像结构层和纹理层。其中, 设计基于RGB空间映射的补偿系数矩阵校正结构层色偏, 并通过滤波分离与融合增强纹理层, 以防止图像的初始特征丢失, 增强后的纹理层与结构层叠加得到第一层输出; 然后, 在对比度均衡模块中, 基于空间变换进行保色对比度限制自适应直方图均衡化, 进一步提高对比度和亮度。最后, 将双层增强结构的结果图融合得到输出图像。通过在不同数据集上与各种经典及新颖的算法对比, 验证本文方法在平衡色差, 增强细节及去模糊等方面均具有更好效果, 对于水下无人系统视觉任务具有实际应用价值。

     

  • 图  1  所提方法流程图

    Figure  1.  Flow chart of the proposed method

    图  2  相对总变差模型分解图像

    Figure  2.  Decomposition results of RTV

    图  3  颜色校正算法结果

    Figure  3.  Color correction results

    图  4  CP-CLAHE效果展示

    Figure  4.  Enhancement and histogram results of the CP-CLAHE

    图  5  定性评价

    Figure  5.  Qualitative evaluation

    图  6  特征点匹配示例

    Figure  6.  Example of feature point matching

    图  7  视频单帧图像增强结果

    Figure  7.  Enhancement results of single frames in video

    表  1  CIEDE2000的评价结果

    Table  1.   Evaluation results for CIEDE2000

    方法D10Z33T6000T8000TS1W60W80平均值
    raw22.18923.26223.27729.08124.34122.0726.17224.342
    UDCP24.40725.07522.40130.88624.64421.16727.93425.216
    Fusion19.63118.94716.45819.51719.34220.18624.54419.804
    GDCP25.48924.12522.7822.90621.14123.03630.84724.332
    Hybrid22.42123.03120.93822.99420.50820.81522.78521.927
    MLLE20.60426.13627.54319.0481822.72921.57822.234
    CFA&MR21.21729.29721.54423.64317.03420.98223.91522.519
    UIESS19.40521.30917.69223.54521.46117.67219.63320.102
    Shallow20.25118.85717.15227.44827.62219.78325.05922.31
    文中方法19.33917.91318.23518.81415.66319.06620.81518.549
    下载: 导出CSV

    表  2  UIEB和EUVP数据集的评价结果

    Table  2.   Evaluation results on UIEB and EUVP databases

    方法UIEB EUVP
    SSIMPSNRFDUMNIQESSIMPSNRFDUMNIQE
    UDCP 0.682 17.065 0.755 2.887 0.631 15.630 0.578 3.637
    GDCP 0.599 13.525 0.854 2.921 0.597 14.306 0.682 3.470
    Fusion 0.768 20.760 0.669 2.781 0.708 19.419 0.495 3.516
    Hybrid 0.641 17.309 1.051 3.245 0.583 16.636 0.790 2.845
    MLLE 0.692 16.111 0.948 3.523 0.631 15.462 0.702 2.940
    CFA&MR 0.574 18.479 0.495 2.920 0.466 17.613 0.340 3.846
    UIESS 0.624 17.811 0.585 4.034 0.687 19.898 0.537 3.985
    Shallow 0.534 16.625 0.455 3.726 0.760 20.238 0.459 4.234
    文中方法 0.789 20.406 0.951 2.832 0.768 20.255 0.820 3.590
    下载: 导出CSV

    表  3  平均运行时间

    Table  3.   Average running time

    方法UDCPFusionGDCPHybridMLLECFA&MRUIESSShallow文中方法
    时间0.6182.6020.38210.5220.0821.5810.6500.1100.365
    下载: 导出CSV

    表  4  特征点匹配结果

    Table  4.   Feature point matching results

    方法UDCPFusionGDCPHybridMLLECFA&MRUIESSShallow文中方法
    数量212937392341373744
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-22
  • 修回日期:  2023-12-17
  • 录用日期:  2024-01-05
  • 网络出版日期:  2024-02-07

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