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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 Sun
Open Access
Regular
  • 46 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

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).

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Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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
  1. 1.Changchun Institute of Optics, Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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