阈值法在毫米波目标辐射信号去噪中的应用研究

范庆辉; 李兴国; 张光锋

doi:10.3724/SP.J.1146.2007.00482

阈值法在毫米波目标辐射信号去噪中的应用研究

doi: 10.3724/SP.J.1146.2007.00482

基金项目:

南京理工大学优秀博士培养基金资助课题

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出版历程
- 收稿日期: 2007-04-02
- 修回日期: 2007-11-09
- 刊出日期: 2008-10-19

The Application of Threshold Denoising to the MMW Target Radiation Signal

摘要

摘要: 小波域阈值法去噪以其效果好，易编程实现而广泛应用到图像及信号的去噪中。该文在分析了毫米波目标辐射信号的小波系数特征后，提出使用非负小波系数代替信号的小波系数。对于确定的阈值，推导了重构信号均方差最小时，非负小波系数的去噪方法，实验表明该文算法具有较好的去噪效果。
- 毫米波目标辐射信号 /
- 硬阈值法 /
- 软阈值法 /
- 均方差
Abstract: Threshold denoising in wavelet domain is an efficient method to reduce the white noise which is easy to program, so that it is widely applied to the image and signal denoising. According to the wavelet transformation characteristic of the MMV target radiation signal, the non-negative wavelet coefficient is used to replace the wavelet coefficient of the signal. For the definite threshold, denoising method of the non-negative wavelet coefficient is inferred when the MSE of the reconfigurable signal is minimized. Experiments show that the method can suppress the noise effectively.
- M /
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[1] 柳薇, 马争鸣. 基于边缘检测的图像小波阈值去噪方法. 中国图象图形学报, 2002, 7(8): 788-793.Liu W and Ma Z M. Wavelet image threshold denoising basedon edge detection. Journal of Image and Graphics, 2002, 7(8):788-793. [2] Chang S G, Yu B, and vetterli M. Adaptive waveletthresholding for image denoising and compression[J].IEEETrans. on Image Proc.2000, 9(9):1532-1546 [3] 杨福生. 小波变换的工程分析与应用. 北京: 科学出版社,2001: 41-50.Yang F S. Engineering Analysis of Wavelets Transform andApplication. Beijing: Science press, 2001: 41-50. [4] 潘泉, 张磊, 孟晋丽, 等. 小波滤波方法及应用. 北京: 清华大学出版社, 2005: 5-10.Pan Q, Zhang L, and Meng J L, et al.. Wavelet FilteringMethod and Its Application. Beijing: Qinghua UniversityPress, 2005: 5-10. [5] Mallat S and Hwang W L. Singularity detection andprocessing with wavelet[J].IEEE Trans. on Information Theory.1992, 38(2):617-643 [6] Xu Y S, et al.. Wavelet transform domain filters: A spatiallyselective noise filtration technique. IEEE Trans. on ImageProcessing, 1994, 3(6): 747-758. [7] Donoho D. Denoising by soft-thresholding[J].IEEE Trans. onInformation Theory.1995, 41(3):613-627 [8] Berkner K and Wells R O. Smoothness estimates forsoft-threshold denoising via translation invariant wavelettransforms[J].Applied and Computational Harmonic Analysis.2002, 12(1):1-24 [9] Donoho D and Johnstone M. Ideal spatial adaptation bywavelet shrinkage[J].Biometrika.1994, 81(3):425-455 [10] Donoho D and Johnstone M. Adapting to unknownsmoothness via wavelet shrinkage[J].Journal of the AmericanStatistical Association.1995, 90(12):1200-1224 [11] Bruce A G and Gao H Y. Understanding wave shrink:variance and bias estimation[J].Biometrika.1996, 83(4):727-746 [12] 李兴国. 毫米波近感技术及其应用. 北京: 国防工业出版社,1991: 35-42.Li X G. Millimeter-wave Near Sensing Technique and ItsApplication. Beijing: National defence industry press, 1991:35-42. [13] 张磊, 潘泉, 张洪才, 戴冠中.小波域滤波阈值参数c 的选取.电子学报, 2001, 29(3): 400-402.Zhang L, Pan Q, and Zhang H C, et al.. On thedetermination of threshold in threshold based denoising bywavelet transform. Acta Electronica Sinica, 2001, 29(3):400-402. [14] 文鸿雁, 张正禄. 非线性小波变换阈值法去噪改进. 测绘通报， 2006, (3): 18-21.Wen H Y and Zhang Z L. Improvement of denoising ofnonlinear wavelet transform threshold value method. Bulletinof Surveying and Mapping, 2006, (3): 18-21. [15] 潘泉, 孟晋丽, 张磊, 等. 小波滤波方法及应用[J].电子与信息学报.2007, 29(1):236-242浏览

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