Detection of dim and small infrared targets based on the improved singular value decomposition

FENG Yang

doi:10.7510/jgjs.issn.1001-3806.2016.03.007

In order to solve the problem of target strengh weakness of traditional target detection method based on singular value decomposition (SVD), an improved SVD algorithm was proposed for background suppression in dim and small infrared target detection. According to the nature of SVD, nonlinear transformation was adopted to improve the middle order part of image singular values for the largest contribution to the goal. And then, the other singular value was set to zero,finally the target image was obtained by reconstructing image. The experimental results show that the proposed method could preserve and enhance the target signal, improve the signal-to-clutter ratio and contrast ratio,and have good performance in complicated background suppression.

Detection of dim and small infrared targets based on the improved singular value decomposition

1. School of Physics and Electrical Engineering, Weinan Normal University, Weinan 714000, China

Received Date: 2015-04-27

Accepted Date: 2015-06-15

Available Online: 2016-05-25

Abstract: In order to solve the problem of target strengh weakness of traditional target detection method based on singular value decomposition (SVD), an improved SVD algorithm was proposed for background suppression in dim and small infrared target detection. According to the nature of SVD, nonlinear transformation was adopted to improve the middle order part of image singular values for the largest contribution to the goal. And then, the other singular value was set to zero,finally the target image was obtained by reconstructing image. The experimental results show that the proposed method could preserve and enhance the target signal, improve the signal-to-clutter ratio and contrast ratio,and have good performance in complicated background suppression.

HTML

Reference (14)

[1]	PENG J X, ZHOU W L. Infrared background suppression for segmenting and detecting small target[J]. Acta Electronica Sinica, 1999, 27(12):47-51(in Chinese).
[2]	ERCELEBI E, KOC S. Lifting-based wavelet domain adaptive wiener filter for image enhancement[J].IEEE Proceedings, Vision, Image and Signal Processing, 2006, 153(1):31-366.
[3]	QIN H L, LIU Sh Q, ZHOU H X, et al. Background suppression for dim small target with Gabor kernel non-local means[J].Infrared and Laser Engineering, 2009,38(4):737-741(in Chinese).
[4]	ZHANG Y, XIN Y H, ZHANG Ch Q.An algorithm based on temporal and spatial filters for infrared weak slow moving point target detection[J]. Acta Photonia Sinica, 2010, 39(11):2049-2054(in Chinese).
[5]	MA W W, DIAO Y J, ZHANG G H. Infrared dim target detection based on multi-structural element morphological filter combined with adaptive threshold segmentation[J]. Acta Photonia Sinica, 2011, 40(7):1020-1024(in Chinese).
[6]	JIANG T, SHEN H L, YANGD D X, et al. Target detection of the images based on C-means of fuzzy local information[J]. Laser Technology, 2015, 39(3):289-294 (in Chinese).
[7]	QIN H L, ZHOU H X, LIU Sh Q, et al. SVD for infrared dim and small target background suppression[J]. Semiconductor Optoelectronics, 2009, 30(3):473-476(in Chinese).
[8]	LV D Y. Research on SVD in digital watermark and infrared small target preprocessing[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2011:41-46(in Chinese).
[9]	HU M F, DONG W J, WANG Sh H, et al. Singular value decomposition band-pass-filter for image background suppression and denoising[J].Acta Electronica Sinica, 2008, 36(1):111-116(in Chinese).
[10]	CUI L J, ZHENG J B, LI X X. Detecting small targets based on SVD for background suppression and particle filter[J]. Application Research of Computers, 2011, 28(4):1553-1555(in Chinese).
[11]	MO J H, XI R P, ZHANG Y N, et al. Global filter combined with local filter for infraredsmall target background suppression[J]. Chinese Journal of Stereology and Image Analysis, 2011, 16(3):223-231(in Chinese).
[12]	LI J L. An introduction of the SVD algorism and its test of artificial data[J]. Annals of Shanghai Observatory Academia Sinca, 1998(19):16-21(in Chinese).
[13]	ZHANG X D. Matrix analysis and application[M]. Beijing:Tsinghua University Press, 2004:341-400(in Chinese).
[14]	ZHANG L, LUO Ch G, ZHANG YY, et al. Fusion algorithm of infrared and visible images based on support value transform[J]. Laser Technology, 2015, 39(3):428-431(in Chinese).

Detection of dim and small infrared targets based on the improved singular value decomposition

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Proportional views