基于多尺度截断的弱小目标复杂背景抑制

周慧鑫; 秦翰林; 赖睿; 刘上乾

doi:doi:10.3788/aos20103010.2812

光学学报, 2010, 30 (10): 2812, 网络出版: 2012-10-24

基于多尺度截断的弱小目标复杂背景抑制

Multiscale Truncation for Dim and Small Target Background Suppression

论文大纲

周慧鑫 ^1,*秦翰林 ¹赖睿 ²刘上乾 ¹

作者单位

¹ 西安电子科技大学技术物理学院，陕西西安 710071

² 西安电子科技大学微电子学院，陕西西安 710071

摘要

红外复杂背景抑制是红外告警等系统发现远距离弱小目标的难题之一。提出了一种将奇异值分解与对偶树复小波变换(DTCWT)相结合的多尺度截断复杂背景抑制新方法。首先采用DTCWT对图像进行正变换，获得图像的多尺度和方向细节特征；然后根据目标和背景杂波信号系数在不同尺度之间的差异，对各子带采用奇异值分解进行处理，并利用最大的特征值重构子带；最后将系数调整后的各子带逆变换到图像域，从而将弱小目标和背景杂波分离，达到抑制背景的目的。实验结果表明，该算法可以在很大程度上抑制结构化背景，保存并增强目标信号。

Abstract

Complex background suppression of infrared dim and small target detection is a key problem for finding long-distance target in infrared warning system. A complex background suppression algorithm based on multiscale truncation, which combines the singular value decomposition with the dual tree complex wavelet transform (DTCWT), is presented. Firstly, DTCWT is adopted to decompose the input infrared image, which extracts multi-scale detail features of images. Then, according to difference between targets and background clutter signals, the singular value decomposition is introduced to process sub-bands, and maximum eigenvalues are utilized to compose the sub-bands. Finally, the image is synthesized by the modified sub-bands, then targets and background details are separated, by which background suppression is realized. Experimental results validate that the presented method could suppress the structured background in some degree, and preserve and enhance the target signal.

参考文献

[1] J. N. Lin, X. Nie, R. Unbehauen. Two-dimensional LMS adaptive filter incorporating a local-mean estimator for image processing［J］. IEEE T. Circ. Systvid, Ⅱ: Analog Digital Signal Processing, 1993, 40(7): 417～428

[2] S. D. Deshpande, M. H. Er, R. Venkateswarlu et al.. Max-mean and max-median filters for detection of small targets［C］. SPIE, 1999, 3809: 74～83

[3] D. S. K. Chan, D. A. Langan, D. A. Stayer. Spatical processing techniques for the detection of small targets in IR clutter［C］. SPIE, 1990, 1305: 53～62

[4] Tom V. T., Peli T. Morphology-based algorithm for point target detection in infrared backgrounds［C］. SPIE, 1993, 1954: 25～32

[5] E. Ercelebi, S. Koc. Lifting-based wavelet domain adaptive wiener filter for image enhancement［J］. Vision, IEE Proceedings, Image and Signal Processing, 2006, 153(1): 31～36

[6] Biyin Zhang, Tianxu Zhang, Zhiguo Cao et al.. Fast new small target detection algorithm based on a modified partial differential equation in infrared clutter［J］. Opt. Engng., 2007, 46(10): 106401-1～6

[7] Ivan W. Selesnick, Richard G. Baraniuk, Nick G. Kingsbury. The dual-tree complex wavelet transform［J］. IEEE Signal Proc. Mag., 2005, 22(6): 123～151

[8] 张贤达. 矩阵分析与应用［M］. 北京: 清华大学出版社, 2004. 341～400

Zhang Xiada. Matrix Analysis and Application［M］. Beijing: Tsinghua University Press, 2004. 341～400

周慧鑫, 秦翰林, 赖睿, 刘上乾. 基于多尺度截断的弱小目标复杂背景抑制[J]. 光学学报, 2010, 30(10): 2812. Zhou Huixin, Qin Hanlin, Lai Rui, Liu Shangqian. Multiscale Truncation for Dim and Small Target Background Suppression[J]. Acta Optica Sinica, 2010, 30(10): 2812.

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