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基于自适应双通道先验的煤矿井下图像去雾算法

王媛彬 韦思雄 段誉 吴华英

王媛彬,韦思雄,段誉,等. 基于自适应双通道先验的煤矿井下图像去雾算法[J]. 工矿自动化,2022,48(5):46-51, 84.  doi: 10.13272/j.issn.1671-251x.2021110053
引用本文: 王媛彬,韦思雄,段誉,等. 基于自适应双通道先验的煤矿井下图像去雾算法[J]. 工矿自动化,2022,48(5):46-51, 84.  doi: 10.13272/j.issn.1671-251x.2021110053
WANG Yuanbin, WEI Sixiong, DUAN Yu, et al. Defogging algorithm of underground coal mine image based on adaptive dual-channel prior[J]. Journal of Mine Automation,2022,48(5):46-51, 84.  doi: 10.13272/j.issn.1671-251x.2021110053
Citation: WANG Yuanbin, WEI Sixiong, DUAN Yu, et al. Defogging algorithm of underground coal mine image based on adaptive dual-channel prior[J]. Journal of Mine Automation,2022,48(5):46-51, 84.  doi: 10.13272/j.issn.1671-251x.2021110053

基于自适应双通道先验的煤矿井下图像去雾算法

doi: 10.13272/j.issn.1671-251x.2021110053
基金项目: 国家自然科学基金面上项目(52174198)。
详细信息
    作者简介:

    王媛彬(1977-),女,河南平顶山人,副教授,博士,主要研究方向为煤矿井下视频监控与装备监测,E-mail:wangyb998@163.com

  • 中图分类号: TD67

Defogging algorithm of underground coal mine image based on adaptive dual-channel prior

  • 摘要: 针对暗通道先验算法在处理煤矿井下图像时存在的图像失真、细节不足和图像暗光等问题,提出了一种基于自适应双通道先验的煤矿井下图像去雾算法。首先,根据大气散射物理模型与煤矿井下特殊环境,建立了煤矿井下尘雾图像退化模型。然后,融合暗通道与亮通道建立双通道先验模型来优化透射率,并加入自适应权重系数来提高透射率图的精度,采用梯度导向滤波代替传统导向滤波对透射率图进行细化处理。最后,结合矿井环境改进大气光值求取方法,根据尘雾图像退化模型复原图像。实验结果表明:该算法能够有效去除图像中的尘雾现象,避免了光晕模糊和过增强现象;相较于暗通道先验算法、Retinex算法、Tarel算法,该算法大幅提升了图像信息熵与平均梯度,使复原后图像的细节信息更加丰富,同时缩短了运行时间。

     

  • 图  1  基于自适应双通道先验的煤矿井下图像去雾算法流程

    Figure  1.  Flow of defogging algorithm for underground coal mine image based on adaptive dual-channel prior

    图  2  不同算法求出的透射率图对比

    Figure  2.  Comparison of transmittance graphs obtained by different algorithms

    图  3  不同算法得到的煤矿井下图像去雾结果对比

    Figure  3.  Comparison of image defogging results of underground coal mine obtained by different algorithms

    表  1  不同算法去雾图像指标比较

    Table  1.   Indicators comparison of defogging images processed by different algorithms

    图像评价指标本文算法暗通道先验算法Retinex算法Tarel算法
    图像1信息熵7.196.587.416.92
    标准差46.8834.3547.6335.12
    平均梯度0.13420.05760.09690.0781
    图像2信息熵7.346.697.487.00
    标准差48.9635.7748.5237.46
    平均梯度0.10710.04870.09610.0682
    图像3信息熵7.476.807.587.09
    标准差43.9434.3146.9836.13
    平均梯度0.09060.04490.07700.0531
    图像4信息熵7.056.367.466.81
    标准差53.6936.9747.4832.11
    平均梯度0.06700.03130.07570.0550
    图像5信息熵7.347.557.697.42
    标准差58.7356.3958.6357.51
    平均梯度0.07340.03570.06330.0362
    下载: 导出CSV

    表  2  不同算法运行时间比较

    Table  2.   Comparison of running time of different algorithms s

    图像本文算法暗通道先验算法Retinex算法Tarel算法
    图像13.686.751.34379
    图像23.386.851.35283
    图像32.186.581.36316
    图像42.596.691.32293
    图像55.688.212.56386
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-20
  • 修回日期:  2022-04-28
  • 网络出版日期:  2022-03-15

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