单帧红外图像弱小目标检测研究综述

凡遵林; 王浩; 管乃洋; 叶婷婷; 孙骞冲

单帧红外图像弱小目标检测研究综述

凡遵林^1,,
王浩²,
管乃洋¹,
叶婷婷³,
孙骞冲^{1, 4, ,}

1.
军事科学院, 北京 100071
2.
天津(滨海)人工智能创新中心, 天津 300000
3.
中国人民解放军96911部队, 北京 100089
4.
国防科技大学计算机学院, 湖南长沙 410073

基金项目:

国家自然科学基金“数理和数据双驱动的红外弱小目标检测技术研究” 62106280

详细信息

作者简介:
凡遵林（1991-），男，助理研究员，主要从事红外技术、光电图像处理和计算机视觉等方面的研究。E-mail：18191261397@163.com

通讯作者:
孙骞冲（1984-），男，博士，主要从事红外技术、计算机视觉和人工智能等方面的研究。E-mail：sunqianchong@hotmail.com

中图分类号: TP391.41
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出版历程
- 收稿日期: 2022-09-22
- 修回日期: 2023-03-27
- 刊出日期: 2023-11-19

Review of Dim Small Target Detection Research in Single Infrared Image

1.
Academy of Military Sciences PLA China, Beijing 100071, China
2.
Tianjin Artificial Intelligence Innovation Center, Tianjin 300000, China
3.
Unit 96911 of Chinese People's Liberation Army, Beijing 100089, China
4.
College of Computer, National University of Defense Technology, Changsha 410073, China

摘要

摘要: 远距离广视角场景中由于红外热成像仪成像原理的局限性、大气环境的干扰、远距离传输介质对红外辐射的衰减，检测目标面临巨大挑战。本文在详细分析了图像背景复杂、目标特性弱小、图像对比度低和结构特性缺失等红外弱小目标图像特性的基础上，从基于目标突显和背景预测两大类概述了单帧红外图像弱小目标检测技术的研究现状，并探讨了红外弱小目标检测研究的发展趋势。
- 红外弱小目标检测 /
- 目标突显 /
- 背景预测 /
- 数理驱动 /
- 数据驱动
Abstract: For long-distance and wide field-of-view scenes, infrared target detection has significant challenges owing to the principle of a thermal imager, interference of the atmospheric environment, and attenuation of infrared radiation by long-distance transmission media. Based on the characteristic analysis of small-target infrared images, such as complex background, dim and small targets, low image contrast, and lack of image structures, we reviewed the research status of infrared dim small-target detection from target highlight and background estimation and discussed the development trend of infrared dim small-target detection.
- infrared dim small target detection /
- target highlight /
- background estimation /
- mathematical driven /
- data driven

HTML全文

图 1 受背景强杂波干扰的红外图像

Figure 1. Infrared images disturbed by strong background clutter

下载: 全尺寸图片幻灯片

参考文献(74)

[1]	BAI X, ZHOU F. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognition, 2010, 43(6): 2145-2156. DOI: 10.1016/j.patcog.2009.12.023
[2]	Jakubowicz J, Lefebvre S, Maire F, et al. Detecting aircraft with a low-resolution infrared sensor[J]. IEEE Transactions on Image Processing, 2012, 21(6): 3034-3041. DOI: 10.1109/TIP.2012.2186307
[3]	凡遵林, 管乃洋, 王之元, 等. 红外图像质量的提升技术综述[J]. 红外技术, 2019, 41(10): 941-946. http://hwjs.nvir.cn/article/id/hwjs201910009 FAN Zunlin, GUAN Naiyang, WANG Zhiyuan, et al. Infrared image quality improvement technology: a review[J]. Infrared Technology, 2019, 41(10): 941-946. http://hwjs.nvir.cn/article/id/hwjs201910009
[4]	杨俊彦, 吴建东, 宋敏敏. 红外成像制导技术发展展望[J]. 红外, 2016, 37(8): 1-6. DOI: 10.3969/j.issn.1672-8785.2016.08.001 YANG Junyan, WU Jiandong, SONG Minmin. Development and prospect of infrared imaging guidance technology[J]. Infrared, 2016, 37(8): 1-6. DOI: 10.3969/j.issn.1672-8785.2016.08.001
[5]	Ahmadi K, Salari E. Small dim object tracking using frequency and spatial domain information[J]. Pattern Recognition, 2016, 58(3): 227-234.
[6]	JIA Y, GAO T, ZHAO J. Texture based infrared military target extraction[J]. Applied Mechanics & Materials, 2011, 44-47: 2489-2493.
[7]	LAW W, XU Z, YONG K. Manganese-doped near-infrared emitting nanocrystals for in vivo biomedical imaging[J]. Optics Express, 2016, 24: 17553-17561. DOI: 10.1364/OE.24.017553
[8]	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(2): 1-21. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZDZ202002001.htm REN Xiangyang, WANG Jie, MA Tianlei, et al. Review on infrared dim and small target detection technology[J]. Journal of Zhengzhou University: Natural Science Edition, 2020, 52(2): 1-21. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZDZ202002001.htm
[9]	ZHENG C, LI H. Small infrared target detection based on harmonic and sparse matrix decomposition[J]. Optical Engineering, 2013, 52(6): 066401. DOI: 10.1117/1.OE.52.6.066401
[10]	ZHANG B, ZHANG T, CAO Z, et al. Fast new small-target detection algorithm based on a modified partial differential equation in infrared clutter[J]. Optical Engineering, 2007, 46(10): 106401. DOI: 10.1117/1.2799509
[11]	BAI X, ZHOU F, XUE B. Infrared dim small target enhancement using toggle contrast operator[J]. Infrared Physics & Technology, 2012, 55: 177-182.
[12]	Bae T, ZHANG F, Kweon I. Edge directional 2D LMS filter for infrared small target detection[J]. Infrared Physics & Technology, 2012, 55(1): 137-145.
[13]	Karalı A, Okman O, Aytaç T. Adaptive enhancement of sea-surface targets in infrared images based on local frequency cues[J]. J. Opt. Soc. Am. A. , 2010, 27(3): 509-517. DOI: 10.1364/JOSAA.27.000509
[14]	Angaitkar P, Saxena P. Enhancement of infrared image: a review[J]. International Journal of Engineering Research and Applications, 2012, 2(2): 1186-1189.
[15]	Janani V, Dinakaran M. Infrared image enhancement techniques - a review[C]//IEEE International Conference on Current Trends in Engineering and Technology, 2014: 167-173.
[16]	Morris N, Avidan S, Matusik W, et al. Statistics of infrared images[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-7.
[17]	Nanhakumar N, Aggarwal J. Integrated analysis of thermal and visual images for scene interpretation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(4): 469-481. DOI: 10.1109/34.3911
[18]	穆为磊, 高建民, 陈富民, 等. 符合人眼视觉特性的焊缝射线数字图像增强方法[J]. 西安交通大学学报, 2012, 46(3): 90-93. DOI: 10.3969/j.issn.1008-245X.2012.03.014 MU Weilei, GAO Jiammin, CHEN Fumin, et al. Weld radiographic image enhancement conforming to human visual system [J]. Journal of Xi'an Jiaotong University, 2012, 46(3): 90-93. DOI: 10.3969/j.issn.1008-245X.2012.03.014
[19]	龚昌来, 罗聪, 杨冬涛, 等. 基于正弦灰度变换的红外图像增强算法[J]. 激光与红外, 2013, 43(2): 200-203. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW201302019.htm GONG Changlai, LUO Cong, YANG Dongtao, et al. Infrared image enhancement based on sine gray level transformation[J]. Laser & Infrared, 2013, 43(2): 200-203. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW201302019.htm
[20]	汪国有, 陈振学, 李乔亮. 复杂背景下红外弱小目标检测的算法研究综述[J]. 红外技术, 2006, 28(5): 287-292. DOI: 10.3969/j.issn.1001-8891.2006.05.010 WANG Gouyou, CHEN Zhenxue, LI Qiaoliang. A review of infrared weak and small targets detection under complicated background[J]. Infrared Technology, 2006, 28(5): 287-292. DOI: 10.3969/j.issn.1001-8891.2006.05.010
[21]	Mahata S, Kar R, Mandal D. Optimal fractional-order highpass Butterworth magnitude characteristics realization using current-mode filter[J]. AEUE-International Journal of Electronics and Communications, 2019, 102: 78-89.
[22]	YANG L, YANG J, YANG K. Adaptive detection for infrared small target under sea-sky complex background[J]. Electronics Letters, 2004, 40(17): 1083-1085. DOI: 10.1049/el:20045204
[23]	DONG X, HUANG X, ZHENG Y. Infrared dim and small target detecting and tracking method inspired by human visual system[J]. Infrared Physics & Technology, 2014, 62: 100-109.
[24]	HAN J, MA Y, HUANG J. An infrared small target detecting algorithm based on human visual system [J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 452-456.
[25]	ZHANG Y, ZHENG L, ZHANG Y. Small infrared target detection via a Mexican-hat distribution[J]. Applied Sciences, 2019, 5: 5570.
[26]	周姣, 辛云宏. 基于显著性与尺度空间的红外弱小目标检测[J]. 激光与红外, 2015, 45(4): 452-456. DOI: 10.3969/j.issn.1001-5078.2015.04.022 ZHOU Jiao, XIN Yunhong. Infrared dim small target detection based on saliency and scale-space[J]. Laser & Infrared, 2015, 45(4): 452-456. DOI: 10.3969/j.issn.1001-5078.2015.04.022
[27]	Srivastava H, Khatterwani K, Upadhyay S. A certain family of fractional wavelet transformations[J]. Mathematical Methods in the Applied Sciences, 2019, 42(9): 3103-3122. DOI: 10.1002/mma.5570
[28]	Boccignone G. Small target detection using wavelets[C]//IEEE Fourteenth International Conference on Pattern Recognition, 1998: 1776-1778.
[29]	Davidson G, Griffiths H. Wavelet detection scheme for small target in sea clutter[J]. Electronics Letters, 2002, 38(19): 1128-1130. DOI: 10.1049/el:20020790
[30]	Candes E. Ridgelets: Theory and Application[D]. Stanford: Stanford University, 1998.
[31]	Starck J, Candes E, Donoho D. The curvelet transform for image denoising[J]. IEEE Transactions on Image Processing, 2002, 11(6): 131-141.
[32]	Do M, Vetterli M. The Contourlet transform: an efficient directional multi-resolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106. DOI: 10.1109/TIP.2005.859376
[33]	Easley G, Labate D, Lim W. Sparse directional image representation using the discrete shearlet transforms[J]. Applied and Computational Harmonic Analysis, 2008, 25: 25-46. DOI: 10.1016/j.acha.2007.09.003
[34]	Lim W. Nonseparable shearlet transform[J]. IEEE Transactions on Image Processing, 2013, 22(5): 2056-2065. DOI: 10.1109/TIP.2013.2244223
[35]	PENG L, ZHANG T, LIU Y, et al. Infrared dim target detection using shearlet's kurtosis maximization under non-uniform background[J]. Symmetry, 2019, 11(5): 723. DOI: 10.3390/sym11050723
[36]	CHEN C, LI H, WEI Y. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581. DOI: 10.1109/TGRS.2013.2242477
[37]	BAI X, BI Y. Derivative entropy-based contrast measure for infrared small-target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2452-2466. DOI: 10.1109/TGRS.2017.2781143
[38]	LI S, LI C, YANG X, et al. Infrared dim target detection method inspired by human vision system[J]. Optik, 2020, 206: 164167. DOI: 10.1016/j.ijleo.2020.164167
[39]	危水根, 王程伟, 张聪炫, 等. 多信息融合的红外弱小目标检测[J]. 红外技术, 2019, 41(9): 857-865. http://hwjs.nvir.cn/article/id/hwjs201909010 WEI Shuigen, WANG Chengwei, ZHANG Congxuan, et al. Infrared dim target detection based on multi-information fusion[J]. Infrared Technology, 2019, 41(9): 857-865. http://hwjs.nvir.cn/article/id/hwjs201909010
[40]	YI X, WANG B, ZHOU H, et al. Dim and small infrared target fast detection guided by visual saliency[J]. Infrared Physics & Technology, 2019, 97: 6-14.
[41]	黄苏琦. 时空谱多特征联合红外弱小目标检测方法研究[D]. 成都: 电子科技大学, 2020. HUANG Suqi. Infrared Dim Small Target Detection Based on Joint Temporal-Spatial-Spectral Features[D]. Chengdu: University of Electronic Science and Technology of China, 2020.
[42]	Serra J. Image Analysis and Mathematical Morphology[M]. New York: Academic Press, 1982.
[43]	BAI X, ZHOU F. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognition, 2010, 43(6): 2145-2156. DOI: 10.1016/j.patcog.2009.12.023
[44]	ZHU H, LIU S, DENG L. Infrared small target detection via low-rank tensor completion with top-hat regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(2): 1004-1016.
[45]	BAI X, ZHOU F. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognition, 2010, 43(6): 2145-2156. DOI: 10.1016/j.patcog.2009.12.023
[46]	刘源, 汤心溢, 李争. 基于新Top Hat变换局部对比度的红外小目标检测[J]. 红外技术, 2015, 37(7): 544-552. http://hwjs.nvir.cn/article/id/hwjs201507002 LIU Yuan, TANG Xinyi, LI Zheng. A new top hat local contrast based algorithm for infrared small target detection[J]. Infrared Technology, 2015, 37(7): 544-552. http://hwjs.nvir.cn/article/id/hwjs201507002
[47]	Ohki S. Two-dimensional LMS adaptive filters[J]. IEEE Trans. Consum Electr, 1991, 37(1): 66-73. DOI: 10.1109/30.73648
[48]	LV P, SUN S, SUN C, et al. Space moving target detection and tracking method in complex background[J]. Infrared Physics & Technology, 2018, 91: 107-118.
[49]	张艺璇, 李玲, 辛云宏. 基于自适应双层TDLMS滤波的红外小目标检测[J]. 光子学报, 2019, 48(9): 186-198. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201909023.htm ZHANG Yixuan, LI Ling, XIN Yunhong. Infrared small target detection based on adaptive double-layer TDLMS filter[J]. Acta Photonica Sinica, 2019, 48(9): 186-198. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB201909023.htm
[50]	Bae T, Sohng K. Small target detection using bilateral filter based on edge component[J]. Journal of Infrared Millimeter and Terahertz Waves, 2010, 31(6): 735-743.
[51]	ZHAO J, CHEN Y, FENG H, et al. Infrared image enhancement through saliency feature analysis based on multi-scale decomposition[J]. Infrared Physics & Technology, 2014, 62: 86-93.
[52]	LU Y, HUANG S, ZHAO W. Sparse representation based infrared small target detection via an online learned double sparse background dictionary[J]. Infrared Physics & Technology, 2019, 99: 14-27.
[53]	GAO C, MENG D, YANG Y, 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. DOI: 10.1109/TIP.2013.2281420
[54]	WANG X, PEN Z, KONG D, et al. Infrared dim target detection based on total variation regularization and principal component pursuit[J]. Image and Vision Computing, 2017, 63: 1-9. DOI: 10.1016/j.imavis.2017.04.002g/guestquery?queryType=xml&restype=unixref&xml=\|Metaphilosophy\|\|38\|2-3\|264\|2007\|\|\|
[55]	ZHANG L, PENG L, ZHANG T, et al. Infrared small target detection via non-convex rank approximation minimization Joint/_2,1 norm[J]. Remote Sensing, 2018, 10(11): 1821. DOI: 10.3390/rs10111821
[56]	DAI Y, WU Y, SONG Y. Infrared small target and background separation via column-wise weighted robust principal component analysis[J]. Infrared Physics and Technology, 2018, 77: 421-430.
[57]	熊斌, 黄心汉, 王敏. 基于自适应目标图像恢复的红外弱小目标检测[J]. 华中科技大学学报: 自然科学版, 2017, 45(10): 25-30. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201710005.htm XIONG Bin, HUANG Xinhan, WANG Min. Infrared small target detection based on adaptive double-layer TDLMS filter[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2017, 45(10): 25-30. https://www.cnki.com.cn/Article/CJFDTOTAL-HZLG201710005.htm
[58]	Bouwmans T, Javed S, Sultana M, et al. Deep neural network concepts for background subtraction: A systematic review and comparative evaluation [J]. Neural Networks, 2019, 117: 8-66. DOI: 10.1016/j.neunet.2019.04.024
[59]	Sheri A, Rafique M, Jeon M, et al. Background subtraction using Gaussian-Bernoulli restricted Boltzmann machine[J]. IET Image Processing, 2018, 12(9): 1646-1654. DOI: 10.1049/iet-ipr.2017.1055
[60]	Farnoosh A, Rezaei B, Ostadabbas S. DeepPBM: Deep probabilistic background model estimation from video sequences [J/OL]. Computer Vision and Pattern Recognition, 2019, https://arxiv.org/abs/1902. 00820v1.
[61]	Akilan T, WU J. sEnDec: An improved image to image CNN for foreground localization[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(10): 4435-4443.
[62]	ZHENG W, WANG K, WANG F. A novel background subtraction algorithm based on parallel vision and Bayesian GANs[J]. Neurocomputing, 2020, 394: 178-200. DOI: 10.1016/j.neucom.2019.04.088
[63]	Lim L, Keles H. Foreground segmentation using convolutional neural networks for multiscale feature encoding [J]. Pattern Recognition Letters, 2018, 112: 256-262. DOI: 10.1016/j.patrec.2018.08.002
[64]	FAN Z, BI D, XIONG L, et al. Dim infrared image enhancement based on convolutional neural network[J]. Neurocomputing, 2018, 272: 396-404. DOI: 10.1016/j.neucom.2017.07.017
[65]	ZHANG T, CAO S, PU T, et al. AGPCNet: attention-guided pyramid context networks for infrared small target detection[J/OL]. Computer Vision and Pattern Recognition, 2021, https://arxiv.org/abs/2111.03580.
[66]	WANG H, ZHOU L, 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), 2019: 8508-8517.
[67]	DAI Y, WU Y, ZHOU F, et al. Asymmetric contextual modulation for infrared small target detection[C]//IEEE Winter Conference on Applications of Computer Vision (WACV), 2021: 949-958.
[68]	LI B, XIAO C, WANG L, et al. Dense nested attention network for infrared small target detection[J/OL]. IEEE Transactions on Image Processing, 2022, Doi: 10.1109/TIP.2022.3199107.
[69]	WANG K, DU S, LIU C, et al. Interior attention-aware network for infrared small target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-13.
[70]	YAN P, HOU R, DUAN X, et al. STDMANet: spatio-temporal differential multiscale attention network for small moving infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5602516.
[71]	HU Y, MA Y, PAN Z, et al. Infrared dim and small target detection from complex scenes via multi-frame spatial–temporal patchtensor model[J]. Remote Sensing, 2022, 14(9): 2234. DOI: 10.3390/rs14092234
[72]	WU T, LI B, LUO Y, et al. MTU-Net: multilevel transUNet for space-based infrared tiny ship detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5601015.
[73]	SONG Z, HUI B. A dataset for infrared image dim-small aircraft target detection and tracking underground/air background[DB/OL]//SCI Data Bank, [2019-10-28]. https://www.scidb.cn/en/detail?dataSetId=720626420933459968&dataSetType=journal.
[74]	SUN X, GUO L, ZHANG W, et al. A dataset for small infrared moving target detection under clutter background [DB/OL]. Science Data Bank, [2023-10-24]. https://cstr.cn/31253.11.sciencedb.j00001.00231.

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