基于均值漂移的自适应滤波及其在光谱信号处理中的应用

刘蓉; 段福庆; 刘三阳; 吴福朝

基于均值漂移的自适应滤波及其在光谱信号处理中的应用

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出版历程
- 收稿日期: 2004-07-27
- 修回日期: 2005-05-24
- 刊出日期: 2006-02-19

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

摘要

摘要: 该文给出了一种基于均值漂移的自适应双边滤波方法，其性能仅取决于空域的核尺度参数，幅度域的核尺度是根据信号的局部特征自适应选取的。该方法能够去除脉冲噪声，能有效抑制非脉冲噪声，并有较强的边缘保护能力。实验和分析表明本文方法的整体性能优于高斯滤波和中值滤波。该文将所提出方法用于天体光谱的去噪，并与均值漂移滤波和小波硬阈值法进行了比较，结果表明：该方法能够有效抑制光谱中天光背景噪声和随机噪声，并能较好地保护谱线信息，更适于天体光谱信号的处理。
- 自适应滤波;均值漂移;双边滤波;光谱;信号处理
Abstract: An adaptive bilateral filtering method based on mean shift algorithm is presented. The filter is governed by the kernel width in spatial domain, which controls the spatial extent of nearby data for filtering. Its kernel width in range domain is chosen adaptively by the local characteristic of the signal. It can remove impulsive noise and improve smoothing of non-impulsive noise with edges preserved. Comparisons with Gaussian filter and median filter were made. Applications to spectra signal processing show this method can suppress noises in spectra effectively and reduce the amount of smoothing near spectral lines.

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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, 黄凌云, 罗曼丽. 一种基于Hough变换和神经网络的分层类星体识别方法.电子科学学刊, 2000, 22(4): 529.535.[12]覃冬梅. 天体光谱信号的自动识别方法研究. 博士论文, 中国科学院自动化所, 2003.

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