Background Suppression of Small Infrared Target Image Based on Nonsubsampled Complex Contourlet Transform and GaussianWavelet Support Vector Regression

doi:10.3969/j.issn.1000-1093.2015.04.017

Abstract

Abstract: For the background suppression problem of dim target infrared image that contains background interference and noise, a new background suppression method based on nonsubsampled complex contourlet transform (NSCCT) and Gaussian wavelet support vector regression (SVR) is presented. With this method, the nonsubsampled complex contourlet transform is performed for the infrared target image, and then the correlation properties of NSCCT coefficients are used to de-noise the image so that the majority of background clutter is suppressed. Gaussian wavelet support vector regression is used to process the denoised infrared image to obtain the predicted image. The predicted image subtracted from the denoised image gives the residual image and the background is suppressed. A large number of experiments are done on infrared images including small targets, and the comparison is made with the background suppression methods of infrared target image based on least squares support vector regression, support vector regression and least squares. The experimental results show that the suggested method can get better de-noising result, and the performance of background suppression is superior.

Key words: information processing technology, infrared search and track, infrared dim target detection, background suppression, nonsubsampled complex contourlet transform, Gaussian wavelet support vector regression

CLC Number:

TN219

WU Yi-quan， SONG Yu. Background Suppression of Small Infrared Target Image Based on Nonsubsampled Complex Contourlet Transform and GaussianWavelet Support Vector Regression[J]. Acta Armamentarii, 2015, 36(4): 687-695.

References

［1］ Zhu F Y，Qin S Y. A moving IR point target detection algorithm based on reverse phase feature of neighborhood in difference between neighbor frame images［J］. Chinese Journal of Aeronautics, 2006, 9(3): 225-232.
［2］ Zhang T X，Li M，Zuo Z R， et al. Moving dim point target detection with three-dimensional wide-to-exact search directional filtering［J］. Pattern Recognition Letters，2007，28(2)：246-253.
［3］陈炳文, 王文伟, 秦前清. 基于分数阶积分算子的红外弱小目标检测［J］. 控制与决策, 2012, 27(1): 147-151.
CHEN Bing-wen, WANG Wen-wei, QIN Qian-qing. Infrared dim target detection based on fractional integral operator［J］. Control and Decision, 2012, 27(1)：147-151. (in Chinese)
［4］曹琦, 毕笃彦. 红外弱小目标检测中的特征选择性滤波方法［J］. 光学学报, 2009, 29(9): 2408-2412.
CAO Qi, BI Du-yan. Characteristic-selecting filtering in infrared small target detection［J］. Acta Optica Sinica，2009, 29(9): 2048-2412. (in Chinese)
［5］靳永亮, 王延杰, 刘艳滢, 等. 红外弱小目标的分割预检测［J］. 光学精密工程, 2012, 20(1): 171-178.
JIN Yong-liang, WANG Yan-jie, LIU Yan-ying, et al. Pre-detection method for small infrared target［J］. Optics and Precision Engineering, 2012, 20(1): 171-178. (in Chinese)
［6］罗寰, 王芳, 陈中起，等. 基于对称差分和光流估计的红外弱小目标检测［J］. 光学学报, 2010, 30(6): 1715-1810.
LUO Huan, WANG Fang, CHEN Zhong-qi, et al. Infrared target detecting based on symmetrical displaced frame difference and optical flow estimation［J］. Acta Optica Sinica, 2010, 30(6): 1715-1810. (in Chinese)
［7］ Xu Y S, Weaver J B, Healy D M. Wavelet transform domain filters: a spatially selective noise filtration technique［J］. IEEE Transactions on Image Processing，1994, 3(6): 747-758.
［8］ Mallat S, Hwang W L. Singularity detection and processing with wavelets［J］. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
［9］ Zhang L, Bao P, Pan Q. Threshold analysis in wavelet-based de-noising［J］. IEEE Electronics Letters，2001, 37(24): 1485-1486.
［10］ Candès E J. Ridgelets: theory and applications［D］. California: Stanford University, 1998.
［11］ Candes E J, Donoho D L. Curvelets-a surprisingly effective non-adaptive representation for objects with edges［R］. Palo Alto, California: Stanford University，1999.
［12］ Minh N D, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation［J］. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
［13］ Selesnick I W, Baraniuk R G, Kingsbury N C. The dual-tree complex wavelet transform［J］. IEEE Signal Processing Magazine, 2005, 22(6): 123-151.
［14］ Arthur L C, Zhou J P, Minh N D. The nonsubsampled contourlet transform: theory, design, and applications［J］. IEEE Transactions on Image Processing, 2006, 15(10)：3089-3101.
［15］ Guo K H, Kutyniol G, Labate D. Sparse multidimensional representations using anisotropic dilation and shear operators［C］∥International Conference on the Interaction between Wavelets and Splines. Athens, GA: Nashboro Press, 2005: 189-201.
［16］秦翰林, 李佳, 周慧鑫, 等. 采用剪切波变换的红外弱小目标背景抑制［J］. 红外与毫米波学报, 2011，30(2)：162-166.
QIN Han-lin, LI Jia, ZHOU Hui-xin, et al. Infrared dim and small target background suppression using shearlet transform［J］. Journal of Infrared Millimeter Waves, 2011, 30(2): 162-166. (in Chinese)
［17］王咏胜, 付永庆. 非抽样复Contourlet变换的构造及其图像去噪应用［J］. 大连海事大学学报, 2009, 35(2)：76-80.
WANG Yong-sheng, FU Yong-qing. Construction of nonsubsampled complex contourlet transform and its application to image denoising［J］. Journal of Dalian Maritime University, 2009, 35(2): 76-80. (in Chinese)
［18］陈新武, 龚均斌, 刘玮, 等. 基于复轮廓波变换的图像消噪［J］. 光电工程, 2009, 36(10): 111-115.
CHEN Xin-wu, GONG Jun-bin, LIU Wei, et al. Image denoising based complex contourlet transform［J］. Opto-Electronic Engineering, 2009, 36(10):111-115. (in Chinese)
［19］王咏胜, 付永庆. 基于非抽样复轮廓波变换的图像去噪算法研究［J］. 光电子·激光, 2009, 20(8): 1118-1122.
WANG Yong-sheng, FU Yong-qing. Study of image denoising based on nonsubsampled complex contourlet transform［J］. Journal of Optoelectronics, 2009, 20(8): 1118-1122. (in Chinese)
［20］秦翰林, 姚柯柯, 周慧鑫, 等. 基于塔型对偶树方向滤波器组的弱小目标背景抑制方法［J］. 兵工学报, 2012, 33(9): 1036-1040.
QIN Han-lin, YAO Ke-ke, ZHOU Hui-xin, et al. Background suppression for dim and small targets based on pyramidal dual-tree directional filter bank［J］. Acta Armamentarii，2012, 33(9): 1036-1040. (in Chinese)
［21］焦建彬, 杨舒, 刘峰, 等. 基于人工神经网络的红外小目标检测［J］. 控制工程, 2010, 17(5): 611-613.
JIAO Jian-bin, YANG Shu, LIU Feng, et al. Small infrared target detection based on artificial neural network［J］. Control Engineering of China, 2010, 17(5): 611-613. (in Chinese)
［22］ Corinna C, Vladimir V. Support-vector networks［J］. Machine-Learning，1995，20(3)：273-297.
［23］ Lin Y, Zhang S M, Cai J Q, et al. Application of wavelet support vector regression on SAR data de-noising［J］. Journal of Systems Engineering and Electronics, 2011, 22(4): 579-586.
［24］荣健, 申金娥, 钟晓春. 基于小波和SVR的红外弱小目标检测方法［J］. 西南交通大学学报, 2008, 43(5)：556-560.
RONG Jian, SHEN Jin-e, ZHONG Xiao-chun. New method for infrared dim target detection based on wavelet and SVR［J］. Journal of Southwest Jiaotong University, 2008, 43(5)：556-560. (in Chinese)
［25］于振华, 蔡远利. 基于在线小波支持向量机回归的混沌时间序列预测［J］. 物理学报, 2006，55(4)：1660-1664.
YU Zhen-hua， CAI Yuan-li. Prediction of chaotic time-series based on online wavelet support vector regression［J］. Acta Physica Sinica，2006，55(4)：1660-1664. (in Chinese)
［26］ Zhang S M, Chen Y. Image denoising based on wavelet support vector regression［J］. Machine Graphics and Vision, 2006, 15(3/4): 673-680.
［27］杨帆, 赵瑞珍, 胡绍海. 基于Contourlet系数相关特性的自适应图像去噪算法［J］. 光学学报, 2009, 29(2): 357-361.
YANG Fan, ZHAO Rui-zhen, HU Shao-hai. Adaptive algorithm for image denoising based on correlation properties of contourlet coefficients［J］. Acta Optica Sinica, 2009, 29(2): 357-361. (in Chinese)

[1]	LIU Yong-qing， JIANG Shuo， YU Dong， SHAO Xiao-tian. Effect of Narrowband Interference Suppression on Pseudorandom Code Zero Value [J]. Acta Armamentarii, 2016, 37(2): 293-298.
[2]	GUAN Si-hai， LI Zhi, HUANG Hui, WANG Zhe. Modified Zero-Attracting l₀-NLMS Algorithm [J]. Acta Armamentarii, 2016, 37(11): 2170-2176.
[3]	JIAO Chuan-hai, LI Yong-cheng, XIE Kai, YANG Yun-fu. Wideband Compressed Blind Sensing without Reconstruction Based on Expectation Deviation and Generalized Likelihood RatioTest [J]. Acta Armamentarii, 2016, 37(10): 1837-1843.
[4]	GUO Ye-cai, WU Hua-peng, WANG Hui, ZHANG Miao-qing. DNA Genetic Bat Algorithm Based Fractionally Spaced Multi-modulus Algorithm [J]. Acta Armamentarii, 2015, 36(8): 1502-1507.
[5]	ZHU Ming-qiang， HOU Jian-jun, LIU Ying ， LI Xu, TIAN Hong-juan. An Time Delay Difference Estimation Algorithm Based on Improved Unscented Particle Filter Suitable for Target Tracking inWireless Sensor Network [J]. Acta Armamentarii, 2015, 36(7): 1266-1272.
[6]	LI Zheng-zhou, HOU Qian, DAI Zhen, FU Hong-xia, GE Feng-zeng, JIN Gang. Dim Moving Target Detection Algorithm Based on Spatial-temporal Sparse Representation [J]. Acta Armamentarii, 2015, 36(7): 1273-1279.
[7]	GUO Ye-cai， ZHANG Miao-qing. A Multi-modulus Blind Equalization Algorithm Based on Shuffled Frog Leaping Algorithm [J]. Acta Armamentarii, 2015, 36(7): 1280-1287.
[8]	LIU Kun, MA Wen-ping, LIU Hong-ying. Speckle Suppression of Polarimetric Synthetic Aperture Radar Images Based on Sublook Data Correlation [J]. Acta Armamentarii, 2015, 36(7): 1288-1294.
[9]	ZHU Xia, CHEN Ren-wen, XIA Hua-kang, ZHANG Piao-yan. A Novel Image Registration Based on Cultural Particle Swarm Optimization Algorithm [J]. Acta Armamentarii, 2015, 36(6): 1033-1039.
[10]	DENG Bing, LUAN Jun-bao. Study of Reconnaissance Reception Algorithm of Wide-band Channelization in the Fractional Fourier Domain [J]. Acta Armamentarii, 2015, 36(6): 1040-1045.
[11]	ZHANG Liang-jun, YANG Jie, LU Kai-wang, SUN Ya-dong. Underdetermined Blind Source Separation Based on Time-frequency Method Using Cyclostationary Characteristic [J]. Acta Armamentarii, 2015, 36(4): 703-709.
[12]	JIANG Run-xiang， LIN Chun-sheng， GONG Shen-guang. Electrostatic Electric Field Inversion Method for Ship Based on Point Charge Source Model [J]. Acta Armamentarii, 2015, 36(3): 545-551.
[13]	WU Zhen， DAI Ji-sheng， ZHU Xiang-lin， ZHAO De-an. A Real-valued Sparse Representation Method for DOA Estimation with Unknown Mutual Coupling [J]. Acta Armamentarii, 2015, 36(2): 294-298.
[14]	ZHANG Dai, ZHANG Yu, YANG Xiao-jing, FAN Bin-bin. An Algorithm for Convolutional Codes Recognition Based on Sectionally Extracting Soft-decision Weighted Walsh HadamardTransform [J]. Acta Armamentarii, 2015, 36(12): 2298-2305.
[15]	YANG Hong， LI Ya-an， LI Guo-hui. Noise Reduction of Chaotic Signal Based on Auxiliary Sigma Point Particle Filter [J]. Acta Armamentarii, 2015, 36(12): 2330-2335.