基于<i>L</i><sub>1−2</sub>时空域总变分正则项的红外弱小目标检测算法

赵德民; 孙扬; 林再平; 熊伟

doi:10.37188/CO.2022-0229

Volume 16 Issue 5

Sep. 2023

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Chinese Optics > 2023 > 16(5): 1066-1080.

ZHAO De-min, SUN Yang, LIN Zai-ping, XIONG Wei. Infrared small target detection via L1−2 spatial-temporal total variation regularization[J]. Chinese Optics, 2023, 16(5): 1066-1080. doi: 10.37188/CO.2022-0229

Citation:

ZHAO De-min, SUN Yang, LIN Zai-ping, XIONG Wei. Infrared small target detection via L₁₋₂ spatial-temporal total variation regularization[J]. Chinese Optics, 2023, 16(5): 1066-1080. doi: 10.37188/CO.2022-0229

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Infrared small target detection via L₁₋₂ spatial-temporal total variation regularization

doi: 10.37188/CO.2022-0229

ZHAO De-min^{1, 2
,},
SUN Yang^{3
,
,},
LIN Zai-ping^3
,,
XIONG Wei^1
,

1.
Aerospace Engineering University, Beijing 150001, China
2.
DFH Satellite Corporation, Beijing 100080, China
3.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds: Supported by National Natural Science Foundation of China (No. 91738302)

More Information

Corresponding author: sunyang_kd@163.com
Received Date: 08 Nov 2022
Rev Recd Date: 01 Dec 2022

Available Online: 14 Apr 2023

Abstract

Abstract

To solve the high false alarms caused by complex background clutters in infrared small-target detection, a novel detection method based on ${L_{1 - 2}}$ spatial-temporal total variation regularization is proposed. First, the input infrared image sequence is transformed into a Spatial-Temporal Infrared Patch-Tensor (STIPT) structure. This step can associate the spatial and temporal information by using the high dimensional data structures in the tensor domain. Then, weighted Schatten p-norm and ${L_{1 - 2}}$ spatial-temporal total variation regularization are incorporated to recover the low-rank background component to preserve the strong edges and corners, which can improve the accuracy of sparse target component recovery. Finally, the STIPT structure can be transformed into an infrared image sequence by the inverse operator, and an adaptive threshold segmentation is used to obtain the real target. The method is verified using a contrast test with other five methods, and the experimental results show that the false alarm rate by this method decreases to 71.4%, 71.7%, 68.5%, 74.3% and 20.47% compared with the Maxemeidan, Tophat, LIRDNet, DNANet and WSNMSTIPT algorithms. The time cost also decreased to 42.4%, 82.9% and 28.7% of that of the Maxemeidan, DNANet and WSNMSTIPT. The extensive experimental results demonstrate the superiority of this method in detection performance, which can greatly improve the accuracy and efficiency of target detection with complex background clutters.

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References(33)

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