Clutter suppression of infrared image based on three-component low-rank matrix decomposition

HE Yu-jie; LI Min; ZHANG Jin-li; YAO Jun-ping

doi:10.3788/OPE.20152307.2069

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Clutter suppression of infrared image based on three-component low-rank matrix decomposition

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- Clutter suppression of infrared image based on three-component low-rank matrix decomposition
- Optics and Precision Engineering Vol. 23, Issue 7, Pages: 2069-2078(2015)
- 作者机构：
  
  1. 第二炮兵工程大学 908教研室,陕西西安,710025
  2. 武警工程大学信息工程系,陕西西安,710086
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152307.2069
  CLC： TP391.4
- Received：26 March 2015，
  
  Revised：06 May 2015，
  
  Published：25 July 2015
- 稿件说明：
移动端阅览
何玉杰, 李敏, 张金利等. 基于低秩三分解的红外图像杂波抑制[J]. 光学精密工程, 2015,23(7): 2069-2078

HE Yu-jie, LI Min, ZHANG Jin-li etc. Clutter suppression of infrared image based on three-component low-rank matrix decomposition[J]. Editorial Office of Optics and Precision Engineering, 2015,23(7): 2069-2078
何玉杰, 李敏, 张金利等. 基于低秩三分解的红外图像杂波抑制[J]. 光学精密工程, 2015,23(7): 2069-2078 DOI： 10.3788/OPE.20152307.2069.

HE Yu-jie, LI Min, ZHANG Jin-li etc. Clutter suppression of infrared image based on three-component low-rank matrix decomposition[J]. Editorial Office of Optics and Precision Engineering, 2015,23(7): 2069-2078 DOI： 10.3788/OPE.20152307.2069.

摘要

针对红外图像中对比度较低、目标信号较弱且受背景噪声杂波干扰较大的特点

结合信号的稀疏表示理论提出了一种基于低秩三分解模型的红外图像背景杂波抑制算法。首先

分别对红外图像中目标、背景和噪声3种成份进行建模描述

得到低秩三分解模型。然后

采用二维高斯模型构造红外小目标超完备字典

利用所提出的低秩三分解模型将分块重置的图像数据矩阵分解为背景、噪声和目标3种成份。最后

对于目标分量进行阈值处理从而得到突出红外小目标的重构图像

实现杂波抑制。在3种不同情况下的实验结果表明:本文算法能够使红外图像局部信噪比提高2倍以上;与其他经典算法相比

抑制因子至少提高15%。得到的结果表明

所提算法能够有效抑制杂波

在提高红外图像信噪比的同时

对不同噪声干扰也具有较强的鲁棒性。

Abstract

To solve the infrared target detection problems caused by low contrast

weak target signals and background clutter interference

a clutter suppression method based on three-component low-rank matrix decomposition model was proposed combined with the sparse representation theory. Firstly

the three components

including target

background and noise

in a infrared image were described respectively to obtain the three-component low-rank matrix decomposition model. Then

an over-complete dictionary for modeling a small target was constructed by using two-dimensional Gaussian model. The three-component low-rank matrix decomposition model was used to decompose the block reset image data into the background

noise and target components. Finally

the target component was processed by thresholding to obtain a reconstructed image with protruded infrared targets and to complete the clutter suppression. The experiments under three conditions demonstrate that the proposed method has increased the local signal to noise ratio of image more than 2 times

and the background suppression factor has increased more than 15% as compared with that of the classical methods. It con cludes that the proposed method not only suppresses the background clutter

improves the signal-to-noise ratio of the infrared image effectively but also has strong robustness against the noise interference.

关键词

Keywords

references

孙海江,王延杰,陈小林. 基于自适应梯度阈值各向异性滤波抑制红外复杂背景[J]. 光学精密工程, 2014, 22(1):146-151. SUN H J, WANG Y J, CHEN X L. Suppression of infrared complex background based on adaptive gradient threshold anisotropic filtering[J]. Opt. Precision Eng., 2014, 22(1): 146-151. (in Chinese)

李一芒,何昕,魏仲慧,等. 采用降维技术的红外目标检测与识别[J]. 光学精密工程, 2013, 21(5):1297-1303. LI Y M, HE X, WEI ZH H, et al.. Infrared target detection and recognition using dimension reduction technology[J]. Opt. Precision Eng., 2013, 21(5): 1297-1303. (in Chinese)

ZENG M, LI J, PENG Z. The design of top-hat morphological filter and application to infrared target detection[J]. Infrared Phys. Technol, 2006,48(1), 67-76.

CAO Y,LIU R M,YANG J.Small target detection using Two-Dimensional Least Mean Square (TDLMS) filter based on neighborhood analysis[J].International Journal of Infrared and Millimeter Waves, 2008, 29(2) :188-200.

DESHPANDE S, ER M H, DONDA V, et al.. Max-mean and max-median filters for detection of small targets[C]. In Proc. Signal and Data Processing of Small Targets, 1999, 74-83.

HU T, ZHAO J J, CAO Y, et al.. Infrared small target detection based on saliency and principle component analysis[J]. J. Infrared Millimeter Waves,2010, 29(4),303-306.

LIU R, ZHI H, Infrared point target detection with Fisher linear discriminant and kernel Fisher linear discriminant [J]. J.Infrared Millimeter Terahertz Waves, 2010,31(12), 1491-1502.

穆治亚, 魏仲慧,何昕,等. 采用稀疏表示的红外图像自适应杂波抑制[J]. 光学精密工程, 2013, 21(7):1850-1857. MU ZH Y, WEI ZH H, HE X, et al.. Adaptive clutter suppression of infrared images by using sparse representation [J]. Opt. Precision Eng., 2013, 21(7): 1850-1857. (in Chinese)

赵佳佳,唐峥远,杨杰,等. 基于自适应子空间重建的杂波抑制[J]. 红外与毫米波学报, 2012, 31(1):47-51. ZHAO J J, TANG ZH Y, YANG J, et al.. Cluster suppression based on adaptive subspace reconstruction[J]. J. Infrared Millimeter Waves, 2012, 31(1):47-51. (in Chinese)

LIU G C, LIN ZH CH, YU Y. Robust subspace segmentation by low-rank representation[C]. In Proc. ICML, 2010,663-670.

LIU G C, LIN ZH CH, SUN J, et al.. Robust recovery of subspace structures by low-rank representation [J]. IEEE Tansactions on Pattern Analysis and Machine Intelligence, 2013,35(1):171-184.

CAI J F, CANDES E J, SHEN Z W. A singular value thresholding algorithm for matrix completion[J]. Siam Journal of Optimization, 2008, 20(4),1956-1982.

BERTSEKAS D. Constrained Optimization and Lagrange Multiplier Methods[M]. USA: Athena Scientific, 1996.

ECKSTEIN J, BERTSEKAS D. On the douglas-rachford splitting method and the proximal point algorithm for maximal monotone operators[J]. Math. Program,1992,55, 293-318.

GAO CH Q, MENG D Y. Infrared patch-image model for small target detection in a single image[J]. IEEE Tansactions on Image Processing, 2013,22(12):4996-5009.

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