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基于IHBF的增强局部对比度红外小目标检测方法

贺顺 谢永妮 杨志伟 贺小艳 刘祥熹

贺顺, 谢永妮, 杨志伟, 贺小艳, 刘祥熹. 基于IHBF的增强局部对比度红外小目标检测方法[J]. 红外技术, 2022, 44(11): 1132-1138.
引用本文: 贺顺, 谢永妮, 杨志伟, 贺小艳, 刘祥熹. 基于IHBF的增强局部对比度红外小目标检测方法[J]. 红外技术, 2022, 44(11): 1132-1138.
HE Shun, XIE Yongni, YANG Zhiwei, HE Xiaoyan, LIU Xiangxi. IHBF-Based Enhanced Local Contrast Measure Methodfor Infrared Small Target Detection[J]. Infrared Technology , 2022, 44(11): 1132-1138.
Citation: HE Shun, XIE Yongni, YANG Zhiwei, HE Xiaoyan, LIU Xiangxi. IHBF-Based Enhanced Local Contrast Measure Methodfor Infrared Small Target Detection[J]. Infrared Technology , 2022, 44(11): 1132-1138.

基于IHBF的增强局部对比度红外小目标检测方法

基金项目: 

国家自然科学基金 62071481

详细信息
    作者简介:

    贺顺(1980-),女,湖南常德人,博士,副教授,主要从事红外图像处理、阵列信号处理等方面的研究工作。E-mail: heshun1212@163.com

    通讯作者:

    谢永妮(1998-),女,陕西宝鸡人,硕士,主要从事红外小目标检测、红外图像处理等研究。E-mail:x1522997@163.com

  • 中图分类号: TP751.1

IHBF-Based Enhanced Local Contrast Measure Methodfor Infrared Small Target Detection

  • 摘要: 针对非均匀背景下红外小目标检测率低的问题,本文引入人眼视觉系统对比度机制,提出一种基于改进高提升滤波(improved high boost filter,IHBF)的增强局部对比度红外小目标检测方法。首先,根据小目标的频域特性,通过IHBF运算提升高频信号同时,剔除含有背景的低频信号;然后,提出增强局部对比度方法构建比差联合形式的算子,进一步增强目标与背景间的对比度,获得最优显著图;最后,采用自适应阈值分割技术获取真实目标。仿真结果表明:相对于现有的局部对比度算法,所提方法在检测率、虚警率等方面更具优势,是非均匀背景下检测红外小目标的一种有效方法。
  • 图  1  检测方法的流程

    Figure  1.  Procedure of the proposed detection method

    图  2  滑动窗口的嵌套模型

    Figure  2.  Nested model of the sliding window

    图  3  本文算法各阶段处理结果

    Figure  3.  The processing and detection results for the six sequences using the proposed algorithm

    图  4  不同算法对比检测结果

    Figure  4.  Detection results of different algorithms on the six sequences

    图  5  不同序列下的各算法ROC曲线

    Figure  5.  ROC curves of different sequences obtained by multiple method

    表  1  六组红外序列的详细信息

    Table  1.   Characteristics of six real infrared sequences

    Seq. Resolution Target size Background type
    1 320×255 about 4×3 Cumulus clutter sky
    2 302×209 about 4×5 High brightness sky
    3 256×256 about 3×5 Ground-tree
    4 127×126 about 4×5 Sea clutters, noises
    5 640×512 about 3×5 Architecture
    6 256×200 about 5×5 Multilayer cloud
    下载: 导出CSV

    表  2  各图像下不同算法的SCRG值和BSF值

    Table  2.   The SCRG and BSF values for each image using different algorithms

    Seq LCM MPCM LIG HB-MLCM NHBF-ILCM Proposed
    SCRG 1 - - 205.92 - - 425.47
    2 - 54.23 79.31 56.64 84.06 109.95
    3 4.21 14.69 17.54 25.35 28.69 48.49
    4 2.15 23.60 25.63 27.49 33.83 69.74
    5 - - 15.14 27.19 - 305.26
    6 6.87 18.56 35.22 123.46 104.23 187.26
    BSF 1 - - 9.13 - - 47.36
    2 - 30.53 75.21 53.03 132.33 174.34
    3 7.91 24.96 27.54 34.81 58.13 70.48
    4 4.99 50.39 62.94 46.75 69.23 103.50
    5 - - 20.58 36.91 - 76.78
    6 14.32 69.96 84.32 89.23 108.47 161.68
    Note: “-” indicates that the algorithm did not detect the target
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-04
  • 修回日期:  2022-09-13
  • 刊出日期:  2022-11-20

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