吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (4): 1347-1352.doi: 10.13229/j.cnki.jdxbgxb201504046

• • 上一篇    下一篇

基于信号子空间的激光主动成像散斑去除

王灿进1, 孙涛1, 王挺峰1, 郭劲1, 刘玉龙2   

  1. 1.中国科学院长春光学精密机械与物理研究所 激光与物质相互作用国家重点实验室,长春 130033;
    2.吉林省烟草专卖局 信息中心,长春130033
  • 收稿日期:2013-11-12 出版日期:2015-07-01 发布日期:2015-07-01
  • 作者简介:王灿进(1987-),男,助理研究员,博士.研究方向:激光主动成像.E-mail:wcjpsh@126.com
  • 基金资助:
    吉林省科技发展计划项目(20126015)

Speckle noise suppression method for laser active imaging based on signal subspace

WANG Can-jin1, SUN Tao1, WANG Ting-feng1, GUO Jin1, LIU Yu-long2   

  1. 1.State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science, Changchun 130033, China;
    2.Jilin Tobacco Monopoly Bureau Information Center, Changchun 130033, China
  • Received:2013-11-12 Online:2015-07-01 Published:2015-07-01

摘要: 为研究激光主动成像中散斑噪声的抑制问题,提出一种基于信号子空间TDC(Time-domain constrained)的散斑去噪方法,并搭建一套基于距离选通ICCD的激光主动照明系统进行实验验证。首先使用同态变换将乘性噪声变为加性噪声,然后利用小波变换估计噪声的协方差;接着对含噪图像进行奇异值分解并估计信号子空间的维数,根据该维数对无噪图像的协方差矩阵进行特征值分解,计算出滤波估计矩阵。将滤波估计矩阵与含噪图像卷积,最后做同态逆变换,得到降噪后的图像。结果证明本文的去噪方法拥有比经典的Lee、Frost和Kuan算法更好的散斑噪声抑制效果,同时计算时间明显缩短。

关键词: 信息处理技术, 激光主动成像, 信号子空间, 散斑噪声, 时域限制, 同态变换

Abstract: To investigate the suppression of the speckle noise in laser active imaging, a denoising method based on Time-Domain Constrained (TDC) in signal subspace is proposed and a laser active imaging system based on range-gating ICCD is constructed for experiment. First, homomorphic transformation is performed to convert the multiplicative noise to additive noise. Second, wavelet transformation is performed to estimate the covariance of speckle noise. Third, the noise image is decomposed into signal subspace and noisy subspace, and singular value decomposing is used to estimate the dimension of the signal subspace. The covariance of clean image is decomposed using eigenvalue decomposing, and denoising estimating matrix is computed. Fourth, the denoising estimating matrix is convolved with noisy image. Finally, the inverse homomorphic transform is carried out to get the denoised image. Experiment results indicate that, compared with classical Lee, Frost and Kuan filtering, the proposed method has advanced denoising performance and consts less computation time.

Key words: information processing, laser active imaging, signal subspace, speckle noise, TDC, homomorphic transformation

中图分类号: 

  • TN249
[1] 王灿进, 孙涛, 石宁宁, 等. 基于双隐含层BP算法的激光主动成像识别系统[J]. 光学精密工程, 2014, 22(6): 1639-1647. Wang Can-jin, Sun Tao, Shi Ning-ning, et al. Laser active imaging and recognition system based on double hidden layer BP algorithm[J]. Opt Precision Eng, 2014, 22(6): 1639-1647.
[2] 钱方, 孙涛, 郭劲, 等. 无参考的特征点复杂度激光干扰图像评估[J]. 光学精密工程, 2015, 23(4): 1179-1186. Qian Fang, Sun Tao, Guo Jin, et al. No-reference laser-dazzling image quality assessment based on feature-point complexity[J]. Opt Precision Eng, 2015, 23(4): 1179-1186.
[3] 赵建川, 王弟男, 陈长青, 等. 红外激光主动成像和识别[J]. 中国光学, 2013, 6(5): 795-802. Zhao Jian-chuan, Wang Di-nan, Chen Chang-qing, et al. Infrared laser active imaging and recognition technology[J]. Chinese Optics, 2013, 6(5): 795-802.
[4] 李自勤,李琦,王骐. 由统计特性分析激光主动成像系统图像的噪声性质[J]. 中国激光, 2004, 31(9):1081-1085. Li Zi-qin, Li Qi, Wang Qi. Noise characteristic in active laser imaging system by statistic analysis[J]. Chinese Journal of Lasers,2004, 31(9): 1081-1085.
[5] Loupas T, McDicken W, Allan P. An adaptive weighted median filter for speckle suppression in medical ultrasonic images[J]. IEEE Trans Circuits System, 1989,36(1):129-135.
[6] Lee J S. Digital image enhancement and noise filtering by use of local statistics[J]. IEEE Trans Pattern Analysis and Machine Intell, 1980, 20: 165-168.
[7] Frost V S, Stiles J A, Shanmugan K S, et al. A mode for radar images and its application to adaptive digital filtering of multiplicative noise[J]. IEEE Trans Pattern Analysis and Machine Intell, 1982, 4: 157-165.
[8] Kuan D, Sawchuk A, Strand T, et al. Adaptive restoration of images with speckle[J]. IEEE Trans Acoust, Speech and Signal Process, 1987, 35(3) : 373-383.
[9] Lu Y H, Tan S Y, Yeo T S, et al. Adaptive filtering algorithms for SAR speckle reduction[C]∥Geoscience and Remote Sensing Symposium, Lincoln,NE, USA, 1996.
[10] Donoho D L. Denoising by soft-thresholding[J]. IEEE TransInform Theory, 1995, 41(3): 613-627.
[11] 叶树亮, 张玉德, 张炜. 齿轮视觉检测中的尺度与方向相关性联合降噪[J]. 光学精密工程, 2014, 22(6): 1622-1630. Ye Shu-liang, Zhang Yu-de, Zhang Wei. Scale and directional correlation combined denoiseing in gear visual inspection[J].Opt Precision Eng, 2014, 22(6): 1622-1630.
[12] Jarabo-Amores P, Rosa-Zurera M, Mata-Moya D, et al. Mean-shift filtering to reduce speckle noise in SAR images[C]∥Instrumentation and Measurement Technology Conference, Singapore, 2009.
[13] Hyenkyun W, Yun S. Alternating minimization algorithm for speckle reduction with a shifting technique[J] . Image Processing,2012,21(4): 1701-1714.
[14] 李晓峰,徐军,罗积军,等. 激光主动成像图像噪声分析与抑制[J]. 红外与激光工程, 2011,40(2): 332-337. Li Xiao-feng, Xu Jun, Luo Ji-jun, et al. Noise analyzing and denoising of intensity image for laser active imaging system[J]. Infrared and Laser Engineering, 2011,40(2): 332-337.
[15] Ikeda S, Ohtsuki T, Tsuji H. Signal-subspace-partition event filtering for eigenvector-based security system using radio waves[C]∥Personal, Indoor and Mobile Radio Communications, Tokyo,Japan,2009.
[16] Gu J, Yang J, Zhang H, et al. Speckle filtering in polarimetric SAR data based on the subspace decomposition[J]. Geoscience and Remote Sensing, 2004, 42(8): 1635-1641.
[17] Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
[18] 邓建青, 刘晶红. 基于Fourier-Mellin变换和Keren算法的改进运动估计算法[J]. 液晶与显示, 2011, 26(3): 364-369. Deng Jian-qing, Liu Jing-hong. Improved motion estimation algorithm based on Fourier-Mellin transform and Keren algorithm[J]. Chinese Journal of Liquid Crystals and Displays, 2011, 26(3): 364-369.
[19] 任志英, 高诚辉, 申丁,等. 双树复小波稳健滤波在工程表面粗糙度评定中的应用[J]. 光学精密工程, 2014, 22(7): 1820-1827. Ren Zhi-ying, Gao Cheng-hui, Shen Ding, et al. Application of DT-CWT robust filtering to evaluation of engineering surface roughness[J]. Opt Precision Eng, 2014, 22(7): 1820-1827.
[20] 任文琦, 王元全. 基于梯度矢量卷积场的四阶各向异性扩散及图像去噪[J]. 光学精密工程, 2013, 21(10): 2713-2719. Ren Wen-qi, Wang Yuan-quan. GVC-based fourth-order anisotropic diffusion for image denoising[J]. Opt Precision Eng,2013, 21(10): 2713-2719.
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