Zhu Deyan, Tang Junwei, Fu Xiaoxuan, Geng Yuanchao, Su Jingqin
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.
Heliyon. 2023 Jun 5;9(6):e16998. doi: 10.1016/j.heliyon.2023.e16998. eCollection 2023 Jun.
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.
红外（IR）小目标检测，尤其是在复杂背景下，在低误报率和高鲁棒性方面仍然面临挑战。本文提出了一种背景减法局部对比度测量（BSLCM）和高斯结构相似性（GSS）集成结构模型来检测红外小目标。在所提出的模型中，BSLCM用于抑制复杂背景并增强目标。进行GSS计算以进一步消除高亮度背景残余和噪声。为了评估所提方法的性能，采用了真实红外序列和七种先进（SOTA）方法。结果表明，BSLCM可以有效抑制各种类型的强背景杂波并增强真实目标。
