Photonic Sensors

, Volume 9, Issue 2, pp 179–188 | Cite as

Infrared LSS-Target Detection Via Adaptive TCAIE-LGM Smoothing and Pixel-Based Background Subtraction

  • Yanfeng Wu
  • Yanjie Wang
  • Peixun Liu
  • Huiyuan Luo
  • Boyang Cheng
  • Haijiang SunEmail author
Open Access


Infrared small target detection is a significant and challenging topic for daily security. This paper proposes a novel model to detect LSS-target (low altitude, slow speed, and small target) under the complicated background. Firstly, the fundamental constituents of an infrared image including the complexity and entropy are calculated, which are invoked as adaptive control parameters of smoothness. Secondly, the adaptive L0 gradient minimization smoothing based on texture complexity and information entropy (TCAIE-LGM) is proposed in order to remove noises and suppress low-amplitude details in infrared image abstraction. Finally, difference of Gaussian (DoG) map is incorporated into the pixel-based adaptive segmentation (PBAS) background modeling algorithm, which can differ LSS-target from the sophisticated background. Experimental results demonstrate that the proposed novel model has a high detection rate and produces fewer false alarms, which outperforms most state-of-the-art methods.


Small target detection L0 smoothing texture complexity information entropy pixel-based adaptive segmentation 



The completion of this paper owes a great deal to the associate editor and anonymous reviewers for their valuable suggestions. All the authors of this paper would like to express their gratitude to CIOMP for its experiment and site support. The paper is jointly supported by the National Natural Science Foundation of China (Grant No. 61602432).


  1. [1]
    J. Hu, Y. Yu, and F. Liu, “Small and dim target detection by background estimation,” Infrared Physics & Technology, 2015, 73: 141–148.ADSCrossRefGoogle Scholar
  2. [2]
    W. P. Yang, X. P. Lu, J. C. Li, and Z. L. Zhang, “Fast algorithm of infrared small target detection in jitter background,” SPIE, 2015, 9476: 947614-1–947614-6.ADSGoogle Scholar
  3. [3]
    H. Deng, J. G. Liu, and Z. Chen, “Infrared small target detection based on modified local entropy and EMD,” Chinese Optics Letters, 2010, 8(1): 24–28.CrossRefGoogle Scholar
  4. [4]
    E. Abdelkawy and D. Mcgaughy, “Small infrared target detection using two-dimensional fast orthogonal search (2D-FOS),” SPIE, 2003, 5094: 179–185.ADSGoogle Scholar
  5. [5]
    T. Xie, Z. Chen, and R. Y. Ma, “A novel method for infrared small target detection based on PGF, BEMD and LIE,” Journal of Infrared & Millimeter Waves, 2017, 36(1): 92–101.Google Scholar
  6. [6]
    D. Y. Huang, A. K. Xue, and Y. F. Guo, “Penalty dynamic programming algorithm for dim targets detection in sensor systems,” Sensors, 2012, 12(4): 5028–5046.CrossRefGoogle Scholar
  7. [7]
    J. L. Gao, C. L. Wen, and M. Q. Liu, “Robust small target Co-detection from airborne infrared image sequences,” Sensors, 2017, 17(10): 2242-1–2242-21.CrossRefGoogle Scholar
  8. [8]
    Z. Z. Li, J. Chen, Q. Hou, H. X. Fu, Z. Dai, R. Z. Li, et al., “Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online,” Sensors, 2014, 14(6): 9451–9470.CrossRefGoogle Scholar
  9. [9]
    S. Kim, “Sea-based infrared scene interpretation by background type classification and coastal region detection for small target detection,” Sensors, 2015, 15(9): 24487–24513.CrossRefGoogle Scholar
  10. [10]
    S. Kim and J. Lee, “Small infrared target detection by region-adaptive clutter rejection for sea-based infrared search and track,” Sensors, 2014, 14(7): 13210–13242.CrossRefGoogle Scholar
  11. [11]
    X. Z. Bai and F. G. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recognition, 2010, 43(6): 2145–2156.CrossRefzbMATHGoogle Scholar
  12. [12]
    P. Wang, J. W. Tian, and C. Q. Gao, “Infrared small target detection using directional highpass filters based on LS-SVM,” Electronics Letters, 2009, 45(3): 156–158.CrossRefGoogle Scholar
  13. [13]
    E. Guariglia, “Entropy and fractal antennas,” Entropy, 2016, 18(3): 1–17.CrossRefGoogle Scholar
  14. [14]
    I. S. Reed, R. M. Gagliardi, and L. B. Stotts, “Optical moving target detection with 3-D matched filtering,” IEEE Transactions on Aerospace & Electronic Systems, 2002, 24(4): 327–336.ADSCrossRefGoogle Scholar
  15. [15]
    X. M. Shen and L. Deng, “Game theory approach to discrete H, filter design,” Signal Processing IEEE Transactions, 1997, 45(4): 1092–1095.ADSCrossRefGoogle Scholar
  16. [16]
    V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, 1997, 22(1): 61–79.CrossRefzbMATHGoogle Scholar
  17. [17]
    Z. J. Liu, C. Y. Chen, and X. B. Shen, “Detection of small objects in image data based on the nonlinear principal component analysis neural network,” Optical Engineering, 2005, 44(9): 093604-1–093604-9.ADSCrossRefGoogle Scholar
  18. [18]
    C. Yao and W. Chen, “Research on infrared dim and small target detection based on improved particle algorithm,” Progress in Laser and Optoelectronics, 2017(11): 143–148.Google Scholar
  19. [19]
    J. Liu and H. Ji, “Infrared dim and small target detection based on mobile weighted pipeline filtering,” Journal of Xi’an Electronic and Science University (NATURAL SCIENCE EDITION), 2007, 34(5): 743–747.Google Scholar
  20. [20]
    Y. Huang, X. F. Zhang, and Y. U. Xin, “Pipeline filtering detection method for photon imaging stationary point target,” Chinese Optics, 2013, 6(1): 73–79.CrossRefGoogle Scholar
  21. [21]
    H. S. Nie, Z. J. Huang, J. T. Diao, J. Chen, H. J. Liu, and Q. Li, “A Wiener filter based infrared small target detecting and tracking method,” in Proceeding of International Conference on Intelligent System Design and Engineering Application, Changsha, China, 2010, pp. 184–187.Google Scholar
  22. [22]
    Y. F. Wu, H. J. Sun, and P. X. Liu, “A novel fast detection method of infrared LSS-Target in complex urban background,” International Journal of Wavelets Multiresolution & Information Processing, 2018, 16(01): 1619–1632.CrossRefzbMATHGoogle Scholar
  23. [23]
    D. A. Scribner, K. A. Sarkady, and M. R. Kruer, “Adaptive retina-like preprocessing for imaging detector arrays,” Proceeding of IEEE International Conference on Neural Networks, 1993(3): 1955–1960.CrossRefGoogle Scholar
  24. [24]
    D. A. Scribner and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” in Proceedings of SPIE: The International Society for Optical Engineering, San Francisco, CA, USA, 1990, pp. 224–233.Google Scholar
  25. [25]
    J. G. Harris, “Nonuniformity correction of infrared image sequences using the constant-statistics constraint,” Image Processing IEEE Transactions, 1999, 8(8): 1148–1151.ADSCrossRefGoogle Scholar
  26. [26]
    H. L. Qin, S. Q. Liu, H. X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” Acta Optica Sinica, 2007, 7(9): 1617–1620.Google Scholar
  27. [27]
    L. Xu, C. W. Lu, Y. Xu, and J. Y. Jia, “Image smoothing via L0 gradient minimization,” ACM Transactions on Graphics, 2011, 30(6): 1–12.Google Scholar
  28. [28]
    C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, 1948: 27(3): 379–423.MathSciNetCrossRefzbMATHGoogle Scholar
  29. [29]
    M. Hofmann, P. Tiefenbacher, and G. Rigoll, “Background segmentation with feedback: the pixel-based adaptive segmenter,” in Proceeding of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA, 2012, pp. 38–43.Google Scholar
  30. [30]
    O. Barnich and M. V. Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Transactions on Image Processing, 2011, 20(6): 1709–1724.ADSMathSciNetCrossRefzbMATHGoogle Scholar
  31. [31]
    Sadarangani and Nikhil, “An improved Gaussian mixture model algorithm for background subtraction,” Massachusetts Institute of Technology, 2002, pp. 1–72.Google Scholar
  32. [32]
    A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. of the IEEE, 2002, 90(7): 1151–1163.CrossRefGoogle Scholar
  33. [33]
    C. Y. Wang and S. Y. Qin, “Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis,” Infrared Physics & Technology, 2015, 69: 123–135.ADSCrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Yanfeng Wu
    • 1
    • 2
  • Yanjie Wang
    • 1
  • Peixun Liu
    • 1
  • Huiyuan Luo
    • 1
    • 2
  • Boyang Cheng
    • 1
    • 2
  • Haijiang Sun
    • 1
    Email author
  1. 1.Changchun Institute of Optics, Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations