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

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...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Liu, Yujia, Liu, Xianyuan, Hao, Xuying, Tang, Wei, Zhang, Sanxing, Lei, Tao
Format: Article
Language:English
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
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3291435