Research on infrared dim and small target detection algorithm based on low-rank tensor recovery

doi:10.23919/JSEE.2023.000004

Abstract

Abstract:

In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection, an improved detection algorithm of infrared small and dim target is proposed in this paper. Firstly, the original infrared images are changed into a new infrared patch tensor mode through data reconstruction. Then, the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics, and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness. Finally, the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image, and the final small target is worked out by a simple partitioning algorithm. The test results in various space-based downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate. It is a kind of infrared small and dim target detection method with good performance.

Key words: complex scene, infrared block tensor, tensor kernel norm, low-rank tensor restoration, weighted inverse entropy, alternating direction method

Chuntong LIU, Hao WANG. Research on infrared dim and small target detection algorithm based on low-rank tensor recovery[J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 861-872.

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Image	Sensor	Resolution	SNR	Detail
Sequence 1	Infrared	640×640	3	Single pixel point target is in the hilly area
Sequence 2	Infrared	640×640	3	Single pixel point target is in the water lake area
Sequence 3	Infrared	640×640	3	Single pixel point target is in the cloud area
Sequence 4	Infrared	640×640	1	Single pixel point target is around the border

Method	Scene
Method	Scene 1	Scene 2	Scene 3	Scene 4-1	Scene 4-2	Scene 4-3
Top-hat	1.09	1.02	2.07	3.79	19.03	40.66
LCM	4.82	5.67	10.87	24.58	10.44	23.78
NIPPS	70.44	259.67	123.34	29.50	25.19	117.56
RIPT	NaN	NaN	NaN	534.31	418.24	746.13
PSTNN	3.10	1.05	2.65	2.66	8.27	14.83
Proposed	438.93	639.53	497.69	Inf	Inf	Inf

Method	Scene
Method	Scene 1	Scene 2	Scene 3	Scene 4-1	Scene 4-2	Scene 4-3
Top-hat	0.92	0.83	0.99	0.07	0.06	0.05
LCM	0.76	0.80	0.73	0.78	0.83	0.76
NIPPS	0.10	0.09	0.16	0	0.01	0.01
RIPT	NaN	NaN	NaN	0	0	0
PSTNN	0.13	0.12	0.19	0.04	0.03	0.03
Proposed	0	0	0	0	0	0

Method	Scene
Method	Scene 1	Scene 2	Scene 3	Scene 4-1	Scene 4-2	Scene 4-3
Top-hat	0.89	0.88	0.87	0.98	0.98	0.91
LCM	12.72	9.14	8.86	8.86	8.62	8.77
NIPPS	618.71	619.26	613.05	612.90	614.34	611.72
RIPT	5.61	7.01	5.89	4.43	4.09	4.18
PSTNN	59.79	59.95	60.29	71.36	71.36	69.98
Proposed	3.90	3.83	3.71	3.78	3.81	4.09

Parameter	Value
$ {\lambda _1} $	0.15
$ {\lambda _2} $	0.11
$ {\lambda _3} $	0.12
${\lambda _{4{{ - } }1} }$	0.36
${\lambda _{4{{ - 2} } } }$	0.36
${\lambda _{4{{ - 3} } } }$	0.36