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Infrared Target-Background Separation Based on Weighted Nuclear Norm Minimization and Robust Principal Component Analysis

Author

Listed:
  • Sur Singh Rawat

    (Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida 201301, India)

  • Sukhendra Singh

    (Department of Information Technology, JSS Academy of Technical Education, Noida 201301, India)

  • Youseef Alotaibi

    (Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia)

  • Saleh Alghamdi

    (Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Gyanendra Kumar

    (School of Computing Sciences and Engineering, Galgotias University, Greater Noida 201310, India)

Abstract

The target detection ability of an infrared small target detection (ISTD) system is advantageous in many applications. The highly varied nature of the background image and small target characteristics make the detection process extremely difficult. To address this issue, this study proposes an infrared patch model system using non-convex (IPNCWNNM) weighted nuclear norm minimization (WNNM) and robust principal component analysis (RPCA). As observed in the most advanced methods of infrared patch images (IPI), the edges, sometimes in a crowded background, can be detected as targets due to the extreme shrinking of singular values (SV). Therefore, a non-convex WNNM and RPCA have been utilized in this paper, where varying weights are assigned to the SV rather than the same weights for all SV in the existing nuclear norm minimization (NNM) of IPI-based methods. The alternate direction method of multiplier (ADMM) is also employed in the mathematical evaluation of the proposed work. The observed evaluations demonstrated that in terms of background suppression and target detection proficiency, the suggested technique performed better than the cited baseline methods.

Suggested Citation

  • Sur Singh Rawat & Sukhendra Singh & Youseef Alotaibi & Saleh Alghamdi & Gyanendra Kumar, 2022. "Infrared Target-Background Separation Based on Weighted Nuclear Norm Minimization and Robust Principal Component Analysis," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2829-:d:883914
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    References listed on IDEAS

    as
    1. Sur Singh Rawat & Saleh Alghamdi & Gyanendra Kumar & Youseef Alotaibi & Osamah Ibrahim Khalaf & Lal Pratap Verma, 2022. "Infrared Small Target Detection Based on Partial Sum Minimization and Total Variation," Mathematics, MDPI, vol. 10(4), pages 1-19, February.
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    Cited by:

    1. Shailendra Pratap Singh & Youseef Alotaibi & Gyanendra Kumar & Sur Singh Rawat, 2022. "Intelligent Adaptive Optimisation Method for Enhancement of Information Security in IoT-Enabled Environments," Sustainability, MDPI, vol. 14(20), pages 1-23, October.

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