Single-Frame Infrared Small Target Detection by High Local Variance, Low-Rank and Sparse Decomposition

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
• Main Authors: Liu, Yujia, Liu, Xianyuan, Hao, Xuying, Tang, Wei, Zhang, Sanxing, Lei, Tao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Decomposition; Detection; high local salience; high local variance; Image edge detection; Infrared imagery; Iterative methods; low-rank and sparse decomposition; Object detection; Optimization; Salience; Single-frame infrared small target detection; singular value; Sparse matrices; Target detection; Tensors; Three-dimensional displays
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3291435

Single-Frame Infrared Small Target Detection (SF-IRSTD) has grown in popularity due to its broad application. Several models based on Low-Rank and Sparse Decomposition (LRSD) have been proposed recently and have shown excellent performance. Nevertheless, these methods regard the non-low-rank sparse points as the targets, obscuring the distinction between the non-low-rank noise and the target in the infrared image. To address this issue, we consider that the targets usually have a high local salience compared to the noise and propose a novel method using High Local Variance, Low-Rank and Sparse Decomposition (HiLV-LRSD), identifying the sparse points with high local salience and non-low-rank as the targets and the remaining regions as the background. Specifically, we first use the local variance to represent local salience and propose an LV* norm to constrain the background's low-rank and local variance. Then, we define an adaptively re-weighted L1 ( L lv ,1 ) norm to constrain the sparsity of the target and enhance the influence of local variance. Finally, we propose an optimization framework and solve it by a Partially Iterative Alternating Direction Method of Multipliers (PI-ADMM). We evaluate our proposed method on the publicly available dataset SIRST and compare it to 10 state-of-the-art SF-IRSTD methods. The results show that our proposed method outperforms these methods.