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Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection

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Abstract

With the development of infrared technology, infrared small targets detection has attracted great interest of researchers. Top-hat filter is one of widely used methods for detecting infrared small target, and the structure elements have great influence on the performance of detection. The structure elements are desired to be adjusted adaptively. To this end, an adaptive structure elements optimization method based on quantum genetic algorithm (QGA) is introduced, and the convergence of QGA reveals the effectiveness of QGA. Experimental results show that the proposed adaptive top-hat filter based on QGA can achieve more stable infrared small target detection performance compared with the traditional top-hat filter.

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Acknowledgements

This work is sponsored by the National Natural Science Foundation (Grant No. 61501259, 61401228), sponsored by China Postdoctoral Science Foundation (Grant No. 2016 M591891, 2015 M581841), sponsored by Postdoctoral Science Foundation of Jiangsu Province(Grant No.1501019A), sponsored by Natural Science Foundation of Jiangsu Province (Grant No. BK20140874, BK20150864), and sponsored by NUPTSF (Grant No. NY214041, NY215136, NY214145).

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Authors and Affiliations

  1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Lizhen Deng, Hu Zhu & Quan Zhou

  2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China

    Yansheng Li

Authors
  1. Lizhen Deng
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  2. Hu Zhu
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  3. Quan Zhou
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  4. Yansheng Li
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Corresponding author

Correspondence to Hu Zhu.

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Deng, L., Zhu, H., Zhou, Q. et al. Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection. Multimed Tools Appl 77, 10539–10551 (2018). https://doi.org/10.1007/s11042-017-4592-2

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  • DOI: https://doi.org/10.1007/s11042-017-4592-2

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