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基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法

盖勇刚

盖勇刚. 基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法[J]. 工矿自动化,2024,50(6):89-95.  doi: 10.13272/j.issn.1671-251x.2024030048
引用本文: 盖勇刚. 基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法[J]. 工矿自动化,2024,50(6):89-95.  doi: 10.13272/j.issn.1671-251x.2024030048
GAI Yonggang. A defogging algorithm for coal mine underground images based on dark channel guided filtering and lighting correction[J]. Journal of Mine Automation,2024,50(6):89-95.  doi: 10.13272/j.issn.1671-251x.2024030048
Citation: GAI Yonggang. A defogging algorithm for coal mine underground images based on dark channel guided filtering and lighting correction[J]. Journal of Mine Automation,2024,50(6):89-95.  doi: 10.13272/j.issn.1671-251x.2024030048

基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法

doi: 10.13272/j.issn.1671-251x.2024030048
基金项目: 辽宁省教育厅基本科研重点攻关项目(JYTZD2023006)。
详细信息
    作者简介:

    盖勇刚(1970—),男,辽宁沈阳人,高级实验师,研究方向为图像处理与模式识别、自动控制系统,E-mail:ggaiyg@sohu.com

  • 中图分类号: TD67

A defogging algorithm for coal mine underground images based on dark channel guided filtering and lighting correction

  • 摘要: 现有煤矿井下图像去雾方法在处理煤矿井下图像时未能在提取图像深层次特征信息的同时进行光照校正,处理后的图像存在细节信息丢失或图像偏暗的问题。提出一种基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法。首先,井下原始图像通过图像分化模块(IDM)进行双边滤波、光照估计和暗原色处理后得到光照图、暗原色图和光照反射图。然后,对暗原色图进行预处理,作为权重引导参数对光照反射图进行引导滤波,以恢复图像细节特征信息。最后,将光照图作为权重参数对图像进行光照校正和特征提取,通过多次光照校正解决颜色失真问题,同时增加网络深度,进而去除黑暗区域的退化,实现图像细节的重构,从而得到清晰图像。主观评价结果表明:基于暗通道引导滤波和光照校正的煤矿井下图像去雾算法在去除雾气的同时,保留了更多的结构纹理及背景细节,使整个图像更加接近于对应的清晰图像。客观评价结果表明:与次优算法PMS−Net相比,在训练集和测试集上信息熵分别提高0.32和0.11,标准差分别提高3.58和1.89,平均梯度分别提高0.008和0.004,说明所提算法可有效降低煤矿井下图像的雾气。消融实验结果表明,所提算法在测试数据集上的信息熵、标准差、平均梯度均高于其他网络组成模型,说明所提算法去雾效果最好,且能有效保留图像细节和边缘信息。

     

  • 图  1  煤矿井下图像去雾模型

    Figure  1.  Coal mines underground image defogging model

    图  2  IDM网络结构

    Figure  2.  Network structure of image differentiation module(IDM)

    图  3  BFM网络结构

    Figure  3.  Network structure of bootstrap filtering module

    图  4  LCM网络结构

    Figure  4.  Network structure of lighting correction module

    图  5  不同算法主观效果对比

    Figure  5.  Comparison of subjective effects of different algorithms

    图  6  不同算法单幅图像的处理速度对比

    Figure  6.  Comparison of processing speeds for single images using different algorithms

    图  7  消融实验主观效果对比

    Figure  7.  Comparison of subjective effects in ablation experiments

    表  1  不同算法客观评价结果

    Table  1.   Objective evaluation results of different algorithms

    算法 训练集 测试集
    信息熵 标准差 平均梯度 信息熵 标准差 平均梯度
    DCP 5.56 35.89 0.055 5.39 32.69 0.052
    PMS−Net 7.55 54.76 0.124 7.32 52.86 0.120
    CEEF 6.45 48.86 0.073 6.02 45.39 0.068
    FFA 6.14 44.67 0.062 5.59 40.42 0.055
    本文算法 7.87 58.34 0.132 7.43 54.75 0.124
    下载: 导出CSV

    表  2  消融实验结果

    Table  2.   Results of ablation experiments

    模型信息熵标准差平均梯度
    w/o IDM4.2340.740.063
    w/o BFM5.7848.940.073
    w/o LCM5,2745.860.078
    完整网络7.4354.750.124
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-03-18
  • 修回日期:  2024-06-15
  • 网络出版日期:  2024-06-21

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