Detection of infrared small target based on background subtraction local contrast measure and Gaussian structural similarity
Deyan Zhu,
Junwei Tang,
Xiaoxuan Fu,
Yuanchao Geng,
Jingqin Su
Affiliations
Deyan Zhu
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China; Key Laboratory of Space Photoelectric Detection and Sensing of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China; Corresponding author. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China.
Junwei Tang
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China; Key Laboratory of Space Photoelectric Detection and Sensing of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China
Xiaoxuan Fu
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China; Key Laboratory of Space Photoelectric Detection and Sensing of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210001, China
Yuanchao Geng
Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang, 621900, China
Jingqin Su
Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang, 621900, China
Infrared (IR) small target detection, especially in a complex background, continues to present challenges in the low false alarm rate and high robustness. In this paper, a background subtraction local contrast measure (BSLCM) and Gaussian structural similarity (GSS) integrated structural model is proposed to detect IR small target. In the proposed model, BSLCM is used to suppress the complex background and enhance the target. GSS calculation is conducted to eliminate the high-brightened background residual and noise further. To evaluate the performance of the proposed method, real IR sequences and seven state-of-the-art (SOTA) methods were adopted. The results demonstrated that the BSLCM can suppress all types of strong background clutter and enhance the true target effectively.