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基于均值漂移的自適應(yīng)濾波及其在光譜信號(hào)處理中的應(yīng)用

劉蓉 段福慶 劉三陽 吳福朝

劉蓉, 段福慶, 劉三陽, 吳福朝. 基于均值漂移的自適應(yīng)濾波及其在光譜信號(hào)處理中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 2006, 28(2): 312-316.
引用本文: 劉蓉, 段福慶, 劉三陽, 吳福朝. 基于均值漂移的自適應(yīng)濾波及其在光譜信號(hào)處理中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 2006, 28(2): 312-316.
Liu Rong, Duan Fu-qing, Liu San-yang, Wu Fu-chao. Mean Shift Based Adaptive Filtering and Its Applications to Spectra Signal Processing[J]. Journal of Electronics & Information Technology, 2006, 28(2): 312-316.
Citation: Liu Rong, Duan Fu-qing, Liu San-yang, Wu Fu-chao. Mean Shift Based Adaptive Filtering and Its Applications to Spectra Signal Processing[J]. Journal of Electronics & Information Technology, 2006, 28(2): 312-316.

基于均值漂移的自適應(yīng)濾波及其在光譜信號(hào)處理中的應(yīng)用

Mean Shift Based Adaptive Filtering and Its Applications to Spectra Signal Processing

  • 摘要: 該文給出了一種基于均值漂移的自適應(yīng)雙邊濾波方法,其性能僅取決于空域的核尺度參數(shù),幅度域的核尺度是根據(jù)信號(hào)的局部特征自適應(yīng)選取的。該方法能夠去除脈沖噪聲,能有效抑制非脈沖噪聲,并有較強(qiáng)的邊緣保護(hù)能力。實(shí)驗(yàn)和分析表明本文方法的整體性能優(yōu)于高斯濾波和中值濾波。該文將所提出方法用于天體光譜的去噪,并與均值漂移濾波和小波硬閾值法進(jìn)行了比較,結(jié)果表明:該方法能夠有效抑制光譜中天光背景噪聲和隨機(jī)噪聲,并能較好地保護(hù)譜線信息,更適于天體光譜信號(hào)的處理。
  • Yu L Y, Wenyuan, Allen Tannenbaum, et al.. Behavioral analysis of anisotropic diffusion in image processing. IEEE Trans on Image Processing, 1996, 5 (11): 15391553. .[2]Joachim Weickert. A review of nonlinear diffusion filtering. Proceedings of the First International Conference on Scale-Space Theory in Computer Vision, Utrecht, The Netherlands, 1997. July 02-04, 1252: 328. .[3]Tomasi C, Manduchi R. Bilateral filtering for gray and colorimages. Proc. Sixth International Conference on Computer Vision, Bombay, India, 1998, January 04 - 07: 839.846.[4]Danny Barash. A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans on PAMI, 2002, 24(6): 844847. .[5]Fukunaga K, Hostetler L D. The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans on information Theory, 1975, 21: 3240. .[6]Comaniciu D, Meer P. Mean shift: a robust approach toward feature space anaysis. IEEE Trans. on PAMI, 2002, 24(5): 603.619.[7]Silverman B W. Density Estimation for Statistics and Data Analysis.[J].New York: Chapman Hall.1986,:-[8]Sheather S J, Jones M C. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society Series B, 1991, 53 (3): 683.690.[9]Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. Proc. Eighth International Conference on Computer Vision, Vancouver, Canada, 2001, 2: 142.149.[10]Wand M P.[J].Jones M. Kernel Smoothing. New York: Chapman Hall.1995,:-[11]周虹, 黃凌云, 羅曼麗. 一種基于Hough變換和神經(jīng)網(wǎng)絡(luò)的分層類星體識(shí)別方法.電子科學(xué)學(xué)刊, 2000, 22(4): 529.535.[12]覃冬梅. 天體光譜信號(hào)的自動(dòng)識(shí)別方法研究. 博士論文, 中國科學(xué)院自動(dòng)化所, 2003.
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  • 收稿日期:  2004-07-27
  • 修回日期:  2005-05-24
  • 刊出日期:  2006-02-19

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