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层次卷积滤波红外弱小目标检测方法

陈灿灿 夏润秋 刘洋 刘力双 陈青山

陈灿灿, 夏润秋, 刘洋, 刘力双, 陈青山. 层次卷积滤波红外弱小目标检测方法[J]. 应用光学.
引用本文: 陈灿灿, 夏润秋, 刘洋, 刘力双, 陈青山. 层次卷积滤波红外弱小目标检测方法[J]. 应用光学.
CHEN Cancan, XIA Runqiu, LIU Yang, LIU Lishuang, CHEN Qingshan. Hierarchical convolution filtering method for infrared dim small target detection[J]. Journal of Applied Optics.
Citation: CHEN Cancan, XIA Runqiu, LIU Yang, LIU Lishuang, CHEN Qingshan. Hierarchical convolution filtering method for infrared dim small target detection[J]. Journal of Applied Optics.

层次卷积滤波红外弱小目标检测方法

基金项目: 国防军工重点计量科研项目(J*********1)
详细信息
    作者简介:

    陈灿灿(1995—),女,硕士,主要从事红外图像小目标检测算法研究。E-mail:chencc2580@163.com 电话:16639013390

  • 中图分类号: TN219

Hierarchical convolution filtering method for infrared dim small target detection

  • 摘要: 针对单帧复杂背景红外图像点目标检测算法存在复杂背景下处理效果不理想、处理时间长的问题,提出了一种层次卷积滤波检测算法。主要分为2个部分:第一,根据红外小目标特性,设计一种层次卷积滤波的算子,对图像进行滤波处理,实现图像中小目标的增效和背景抑制的效果;第二,采用基于最大值的自适应阈值方法,对图像进行二值化操作,过滤背景杂波,最终提取到待检测的目标。在大量不同背景红外图像中进行实验,论文算法在背景抑制因子和信噪比增益的性能量化结果上优于现有5种典型红外弱小目标检测算法的性能结果,且平均处理时间上仅为高斯拉普拉斯(laplacian of gaussian,LoG)滤波算法的30.42%。通过实验对比,表明该层次卷积滤波算法可以有效解决在不同复杂背景下的红外图像中对小目标检测的问题。
  • 图  1  整体算法流程

    Fig.  1  Overall algorithm flow

    图  2  含有小目标的红外图像

    Fig.  2  Infrared image with small targets

    图  3  算子模型介绍

    Fig.  3  Introduction of operator model

    图  4  该算子的不同应用情况

    Fig.  4  Different applications of this operator

    图  5  6种算法处理后的单帧图像

    Fig.  5  Single frame image processed by six algorithms

    图  6  6种算法处理后的序列图像

    Fig.  6  Sequence images processed by six algorithms

    图  8  不同算法的ROC曲线图

    Fig.  8  ROC curves of different algorithms

    表  1  红外序列图像信息

    Table  1  Infrared sequence image information

    图像序列帧数图像大小目标数量目标尺寸
    Seq.6325256×25613×3
    Seq.7597256×25613×3
    Seq.8266256×25613×3
    Seq.9276256×25613×3
    Seq.10290256×25613×3
    下载: 导出CSV

    表  2  6种方法在Seq.6-10上的SCRG值比较

    Table  2  Comparison of SCRG values of six methods on Seq.6-10

    算法图像序列
    Seq.6Seq.7Seq.8Seq.9Seq.10
    TopHat1 501.5778.900.931 062.4412.55
    IPI24 184.64332.688.6670.4919.08
    LCM7 971.7512.414.098 441.1229.78
    MPCM10 099.6161.6810.6710 891.9355.73
    LoG3 613.2281.992.22 967.215.06
    Ours30 663734.6528.1416 763.9657.51
    下载: 导出CSV

    表  3  6种方法在Seq.6-10上的BSF值比较

    Table  3  Comparison of BSF values of six methods on Seq.6-10

    算法图像序列
    Seq.6Seq.7Seq.8Seq.9Seq.10
    TopHat268.01228.77265.74246.53639.97
    IPI25.1910.4810.3514.8712.84
    LCM165.27134.29173.25193.29178.09
    MPCM124.59327.89227.86207.17563.92
    LoG555.63421.91493.86506.001 182.76
    Ours888.351 503.298 004.541 231.683 992.90
    下载: 导出CSV

    表  4  6种方法在Seq.6-10中平均运行时间

    Table  4  Average running time of six methods in Seq.6-10 1s

    算法图像序列
    Seq.6Seq.7Seq.8Seq.9Seq.10
    TopHat0.010 00.010 60.010 50.010 90.011 6
    IPI6.925 67.152 97.287 37.041 58.189 0
    LCM0.081 50.083 00.081 40.084 20.086 0
    MPCM0.068 30.068 20.066 70.068 90.068 8
    LoG0.019 20.020 70.020 60.020 00.020 9
    Ours0.006 40.006 40.005 90.005 60.006 5
    下载: 导出CSV
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