红外小目标检测作为红外探测系统的核心技术，在军用和民用领域中发挥着越来越重要的作用。在实际场景中，由于被测目标通常与红外探测器之间的距离比较远，使得目标在原始红外图像中所占像素较少，几乎没有明显的特征，导致目标精确检测十分困难。人类视觉对比度可以快速排除无关信息的干扰，锁定对比度突出的目标区域，有助于准确高效地检测到小目标。因此，本文围绕基于视觉对比度的红外小目标检测方法展开研究，主要研究内容如下：

     （1）针对红外小目标易受碎积云层、重杂波、高亮度边缘等非均匀背景影响而检测率低的问题，提出一种基于改进高提升滤波的增强局部韦伯对比度（IHBF-ELWCM）检测方法。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；再利用 ELWCM 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；在此基础上，采用自适应阈值分割技术获取真实目标。实验结果表明：所提方法能够提高小目标在非均匀背景下的可见度，减小复杂边缘突变引入的虚假检测，具有更加优良的检测性能。

     （2）针对局部对比度方法在利用大尺度窗口检测目标时容易出现块效应的问题，提出一种基于梯度加权的多尺度区域对比度（GW-MRCM）检测方法。通过对待检测图像进行区域对比度处理，增强目标区域的视觉显著性；并充分考虑目标大小，利用新定义的 MRCM 方法来优化红外图像的显著性映射，同时避免块效应的产生；再根据目标与背景区域的梯度方向差异，采用局部梯度算子将边缘较强的背景信息进行有效滤除，进一步提高小目标的检测率。实验结果表明：所提方法不仅能够有效抑制目标块效应，而且在检测率和虚警率等方面可以达到很好的性能，是红外小目标检测的一种有效方法。

      Infrared small target detection is the core technology of infrared detection system, which plays an increasingly important role in military and civilian fields. In the actual scene, the target is usually far from the infrared detector, which makes the target's few pixels in the original infrared image, and there are almost no obvious features, resulting in the difficulty of precise target detection. The human visual contrast can quickly eliminate the interference of irrelevant information, locate the target area with prominent contrast, and accurately and efficiently detect small targets. Therefore, this paper focuses on the infrared small target detection method based on visual contrast. The main research contents are as follows:

      (1) Aiming at the infrared small targets are susceptible to non-uniform backgrounds such as cloud layers, heavy clutter, and high-brightness edges, resulting in low detection rate, an improved high boost filter-based enhanced local Weber contrast measure (IHBF-ELWCM) method is proposed. Based on the frequency characteristics of the small target, the IHBF operation is used to improve the high frequency signal while discarding the low-frequency signal containing the background. Then, an ELWCM method is proposed to construct the contrast operator of the ratio-difference joint form. Thus, the contrast between the target and the background can be enhanced further to obtain an optimal saliency map. On this basis, the adaptive threshold technology is used to extract small targets. The experimental results show that the proposed method can increase the visibility of the targets in the clutter backgrounds, reduce the false alarm caused by the abrupt region of complex edge, and has better detection performance.

        (2) Aiming at the block effect is easily introduced when local contrast method used large-scale window to detect the target, a gradient weighted-based multiscale region contrast measure (GW-MRCM) method is proposed. Through the regional contrast processing of the image to be detected, the visual significance of the target area is enhanced, and the target size are fully considered, the newly defined MRCM method is used to optimize the saliencymapping of the infrared image, while avoiding the block effect. Then, according to the gradient direction difference between the target and the background area, the local gradient operator is used to effectively filter the background information with strong edge, and the small target detection rate is further improved. The experimental results show that the proposed method can not only effectively suppress the target block effect, but also achieve better performance in terms of detection rate and false alarm rate. It is an effective method for infrared small target detection.

[1]Moradi S, Moallem P, Sabahi M F, et al. Fast and robust small infrared target detection using absolute directional mean difference algorithm[J]. Signal Processing, 2020, 177(06):107727.

[2]Hou F J, Zhang Y, Zhou Y. Review on infrared imaging technology[J]. Sustainability, 2022, 14(18):11161.

[3]Yao S B, Zhu Q Y, Zhang T, et al. Infrared image small-target detection based on improved FCOS and spatio-temporal features[J]. Electronics, 2022, 11(06):933.

[4]张晔. 结合频域显著性分析和形态学滤波的红外小目标检测算法[J]. 激光与红外, 2022, 52(10):1487-1493.

[5]Chen Y W, Song B, Wang D J, et al. An effective infrared small target detection method based on the human visual attention[J]. Infrared Physics and Techonlogy, 2018, 95(01):128-135.

[6]任向阳, 王杰, 马天磊等. 红外弱小目标检测技术综述[J]. 郑州大学学报(理学版), 2020, 52(02):1-21.

[7]何青叶. 基于单帧图像的红外弱小目标检测技术研究综述[J]. 红外, 2022, 43(04):9-19.

[8]Chen Q X. Summary about detection and tracking of infrared small targets[J]. Intelligent Computation Technology and Automation, 2019, 12(06):250-253.

[9]Reed I S, Gagliardi R M, Stotts L B. Optical moving target detection with 3-D matched filtering[J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(04):327-33.

[10]Ren X Y, Wang J, Ma T L, et al. Infrared dim and small target detection based on three-dimensional collaborative filtering and spatial inversion modeling[J]. Infrared Physics and Technology, 2019, 101:13-24.

[11]Guo Q, Li Z W, Song W M, et al. Parallel computing based dynamic programming algorithm of track-before-detect[J]. Symmetry, 2019, 11(01):29.

[12]张雅楠, 陈绪光, 许文海. 海面弱小目标红外检测方法的高速实现[J]. 光电子激光, 2019, (05):516-521.

[13]Lv P Y, Sun S L, Lin C Q, et al. Space moving target detection and tracking method in complex background[J]. Infrared Physics and Technology, 2018, 91:107-118.

[14]Deshpande S D, Er M H, Ronda V, et al. Max-Mean and Max-Median filters for detection of small targets[C]//Processing of small targets international society for optics and photonics, Denver, Colorado, 1999.

[15]Bai X Z, Zhou F G. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognition, 2010, 43(06):2145-2156.

[16]Wang W G, Li C M, Shi J N. A robust infrared dim target detection method based on template filtering and saliency extraction[J]. Infrared Physics and Technology, 2015, 73(11):19-28.

[17]Marvasti F S, Mosavi M R, Nasiri M. Flying small target detection in IR images based on adaptive toggle operator[J]. IET Computer Vision, 2018, 12(04):527-534.

[18]Zhu H, Zhang J K, Xu G X, et al. Balanced ring Top-Hat transformation for infrared small-target detection with guided filter kernel[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(05):3892-3903.

[19]吴涛, 何文忠, 陈晓露. 基于局部特征的单帧红外小目标检测算法[J]. 激光与红外, 2016, 46(03):368-371.

[20]Garcia A F. Track-before-detect labeled multi-bernoulli particle filter with label switching[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 52:2123-2138.

[21]Huang S Q, Liu Y H, He Y M, et al. Structure-adaptive clutter suppression for infrared small target detection: chain-growth filtering [J]. Remote Sensing, 2020, 12(01):47.

[22]张晔. 结合频域显著性分析和形态学滤波的红外小目标检测算法[J]. 激光与红外, 2022, 52(10):1487-1493.

[23]Gao C Q, Meng D Y, Yang Y T, et al. Infrared patch-image model for small target detection in a single image[J]. IEEE Transactions on Image Processing, 2013, 22(12):4996-5009.

[24]He Y J, Li M, Zhang J L, et al. Small infrared target detection based on low-rank and sparse representation[J]. Infrared Physics and Technology, 2015, 68:98-109.

[25]Zhang L D. Infrared small target detection via non-convex rank approximation joint norm[J]. Remote Sensing, 2018, 10(11):1821-1845.

[26]孙青, 李玲, 辛云宏. 基于局部多尺度低秩分解的红外小目标检测算法[J]. 激光与红外, 2019, 49(03):369-375.

[27]Deng H, Liu J G, Chen Z. Infrared small target detection based on modified local entropy and EMD[J]. Chinese Optics Letters, 2010, 8(01):24-28.

[28]Yao Q, Bruzzone L, Gao C Q, et al. Infrared small target detection based on facet kernel and random walker[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(09):7104-7118.

[29]Kim S. Analysis of Small Infrared target features and learning-based false detection removal for infrared search and track[J]. Pattern Analysis and Applications, 2014, 17(04):883-900.

[30]Bi Y G, Bai X Z, Tian J, et al. Multiple feature analysis for infrared small target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(08):1333-1337.

[31]Wang H, Zhou L P,Wang L. Miss detection vs. false alarm: adversarial learning for small object segmentation in infrared images[C]//IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019.

[32]王恒慧, 曹东, 赵杨等. 基于深度学习的红外弱小目标检测算法研究综述[J]. 激光与红外, 2022, 52(09):1274-1279.

[33]王瑞, 朱志宇, 张冰. 基于人类视觉机制的红外目标检测方法[J]. 火力与指挥控制, 2017, 42(10):138-146.

[34]Zhang H, Zhang L, Ding Y, et al. Infrared small target detection based on local intensity and gradient[J]. Infrared Physics and Technology, 2017, 89 (12):88-96.

[35]Kim S, Yang Y, Lee J, et al. Small target detection utilizing robust methods of the human visual system for IRST[J]. Infrared Millimeter and Terahertz Waves, 2009, 30:994-1011.

[36]Shao X P, Fan H, Lu G X, et al. An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system[J]. Infrared Physics and Technology, 2012, 55(05):403-408.

[37]Wang X, Lv G F, Xu L Z. Infrared dim target detection based on visual attention[J]. Infrared Physics and Technology, 2012, 55(06):513-521.

[38]Qi S X, Jie M, Chao T. A robust directional saliency-based method for infrared small target detection under various complex backgrounds[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(03):495-499.

[39]Chen CLP, Li H, Wei Y T, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(01):574-581.

[40]王晓阳, 彭真明, 张萍, 等. 局部对比度结合区域显著性红外弱小目标检测[J]. 强激光与粒子束, 2015, 27(9):38-44.

[41]张祥越, 丁庆海, 罗海波等. 基于改进LCM的红外小目标检测算法[J]. 红外与激光工程, 2017, 46(07):270-276.

[42]Qin Y, Li B. Effective infrared small target detection utilizing a novel local contrast method[J]. IEEE geoscience and remote sensing letters, 2016, 13(12): 1890-1894.

[43]Deng H, Sun X P, Liu M L, et al. Small infrared target detection based on weighted local difference measure[J]. IEEE transactions on geoscience and remote sensing, 2016, 54(7):4204-4214.

[44]蒋国清, 万烂军. 基于最恰对比度显著性分析的红外弱小目标检测方法[J]. 红外与激光工程, 2021, 50(04):265-272.

[45]刘旭, 崔文楠. 采用人类视觉对比机制的红外弱小目标检测[J]. 红外技术, 2020, 42(06):559-565.

[46]Wang H, Liu C T, Ma C N, et al. A novel and high-speed local contrast method for infrared small-target detection[J]. 2020, 17(10): 1812–1816.

[47]马鹏阁, 魏宏光, 孙俊灵等. 基于LOG滤波的增强局部对比度红外小目标检测方法[J/OL].兵工学报, 2023-04-25.

[48]Han J H, Ma Y, Zhou B, et al. A robust infrared small target detection algorithm based on human visual system[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12):2168-2172.

[49]Wei Y T, You X, Li H. Multiscale patch-based contrast measure for small infrared target detection[J]. Pattern Recognition, 2016, 58: 216–226.

[50]Gao J Y, Guo Y L, Lin Z P, et al. Robust Infrared small target detection using multiscale gray and variance difference measures[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(12):5039-5052.

[51]Guan X W, Peng Z M, Huang S Q, et al. Gaussian scale-space enhanced local contrast measure for small infrared target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(02):327-331

[52]Shi Y F, Wei Y T, Yao H, et al. High-boost-based multiscale local contrast measure for infrared small target detection[J]. 2018, 15 (01):33–37.

[53]Han J H, Liang K, Zhou B, et al. Infrared small target detection utilizing the multiscale relative local contrast measure[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(04): 612-616.

[54]蔡军, 黄袁园, 李鹏泽, 等.基于视觉对比度机制的红外弱小目标检测算法[J]. 系统工程与电子技术, 2019, 41(11):2416-2423.

[55]潘胜达, 张素, 赵明, 等.基于双层局部对比度的红外弱小目标检测方法[J]. 光子学报, 2020, 49(01):0110003.

[56]Qiu Z B, Ma Y, Fan F, et al. Adaptive scale patch-based contrast measure for dim and small infrared target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 19:245-252.

[57]李德新, 钟洪. 基于差异梯度直方图与显著性映射的红外弱小目标检测算法[J]. 光学技术, 2021, 47(05):594-600.

[58]吴安茂, 骆定辉. 红外探测技术的应用及发展[J]. 电子技术与软件工程, 2019, 156(10):88.

[59]Tao G, Zhao X M, Chen T, et al. Image feature representation with orthogonal symmetric local weber graph structure[J]. Neurocomputing, 2017, 240 (03): 70-83.

[60]Huang K Q, Tao D C, Yuan Y, et al. Biologically inspired features for scene classification in video surveillance[J]. IEEE Transactions on Systems, Man, and Cybern. B, Cybernetics, 2011, 41(01):307–313.

[61]Han J H, Liu C Y, Liu Y C, et al. Infrared small target detection utilizing the enhanced closest-mean background estimation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14(05):645-662.

[62]He Y F, Zhang C M, Mu T K, et al. Multiscale local gray dynamic range method for infrared small-target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(10):1846-1850.