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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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 61; s. 1
Hlavní autori: Liu, Yujia, Liu, Xianyuan, Hao, Xuying, Tang, Wei, Zhang, Sanxing, Lei, Tao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3291435