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Infrared Small Target Detection Based on Partial Sum Minimization and Total Variation

Author

Listed:
  • Sur Singh Rawat

    (JSS Academy of Technical Education, Noida 201301, India)

  • 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 201306, India)

  • Youseef Alotaibi

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

  • Osamah Ibrahim Khalaf

    (Al-Nahrain Nano-Renewable Energy Research Center, Al-Nahrain University, Baghdad 10001, Iraq)

  • Lal Pratap Verma

    (Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 302004, India)

Abstract

In the advanced applications, based on infrared detection systems, the precise detection of small targets has become a tough work today. This becomes even more difficult when the background is highly dense in addition to the nature of small targets. The problem raised above is solved in various ways, including infrared patch image (IPI) based methods which are considered to have the best performance. In addition, the greater shrinkage of singular values in the methods based on IPI leads to the problem of nuclear norm minimization (NNM), which leads to the problem of incorrectly recognizing small targets in a highly complex background. Hence, this paper proposed a new method for infrared small target detection (ISTD) via total variation and partial sum minimization (TV-PSMSV). The proposed TV-PSMVS in this work basically replaces the IPI’s NNM with partial sum minimization (PSM) of singular values and, additionally, the total variance (TV) regularization term is inducted to the background patch image (BPI) to suppress the complex background and enhance the target object of interest. The mathematical solution of the proposed TV-PSMSV approach was performed using alternating direction multiplier (ADMM) to verify the proposed solution. The experimental evaluation using real and synthetic data set was performed, and the result revealed that the proposed TV-PSMSV outperformed existing referenced methods in the terms of background suppression factor ( BSF ) and the signal to gain ratio ( SCRG ).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:671-:d:754550
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    Citations

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    Cited by:

    1. Kuruva Lakshmanna & Neelakandan Subramani & Youseef Alotaibi & Saleh Alghamdi & Osamah Ibrahim Khalafand & Ashok Kumar Nanda, 2022. "Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    2. 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.
    3. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.

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