一种提高共聚焦显微镜信噪比算法的研究

胡茂海, 杨晓春

胡茂海, 杨晓春. 一种提高共聚焦显微镜信噪比算法的研究[J]. 应用光学, 2010, 31(1): 70-72.
引用本文: 胡茂海, 杨晓春. 一种提高共聚焦显微镜信噪比算法的研究[J]. 应用光学, 2010, 31(1): 70-72.
HU Mao-hai, YANG Xiao-chun. Algorithm of improving confocal microscope SNR[J]. Journal of Applied Optics, 2010, 31(1): 70-72.
Citation: HU Mao-hai, YANG Xiao-chun. Algorithm of improving confocal microscope SNR[J]. Journal of Applied Optics, 2010, 31(1): 70-72.

一种提高共聚焦显微镜信噪比算法的研究

详细信息
    通讯作者:

    胡茂海(1967-),男,安徽人,副教授,博士,主要从事生物光子学及显微成像技术研究。

  • 中图分类号: TN713;TH742

Algorithm of improving confocal microscope SNR

  • 摘要: 基于共焦显微镜的成像特点,建立了Kalman滤波算法的理论模型,把Kalman滤波方法引入到系统中,提出一种基于图像像素的Kalman滤波算法,并实现了实时化的Kalman滤波器。实验结果表明:该算法能够有效地提高共焦显微镜信噪比,但是以牺牲时间为代价,提高系统分辨率的根本方法还是要着重考虑优化成像系统光路和探测电路。
    Abstract: A theoretical model for Kalman filtering algorithm is established based on the imaging characteristic of the confocal microscope. The Kalmam filtering algorithm based on the image pixel is proposed and a realtime Kalman filter is realized by means of the introduction of Kalman filtering method into the system. The experimental result shows that the algorithm can effectively improve the signal-noise-ratio (SNR) for the confocal microscopy imaging systems, but it is obtained at the expense of time. Therefore, the perfect method to improve the resolution of the system is to emphatically consider the optimization of the beam path of the imaging system and the detection circuit.
  • [1]FRIED D L. Resolution, signal-to-noise-ratio, and measurement precision[J]. J. Op t. Soc. Am, 1979,69(3):399-406.
    [2]FRIED D L. Resolution, signal-to-noise-ratio, and measurement precision, addendum[J]. J. Opt. Soc.Am, 1980,70(6):748-749.
    [3]SANDISON D R,WEBB W W. Background rejec-tion and signal-to-noise optimization in confocal and alternative fluorescence micro-scopes[J]. Applied Optics, 1994,33(4):603-615
    [4]SCHMITT T, GEBAUER H D, FREYER R. Re-storation of nuclear medicine images using a Kalman filtering approach[J]. SPIE,1995,2421:64-69.
    [5]LAPLANTE P A,NEILL C J. A class of Kalman filters for real-time image processing[J]. SPIE,2003,5012:23-29.
    [6]DZIELINSKI A, SKONECZNY S. Recurrent neural network application to image filtering. 2-D Kalman filtering approach[J]. SPIE,1991,1451:234241.
    [7]TAO Hong-jiu, WANG Yue, ZHOU Zu-de. Study of image super resolution reconstruction algorithm based on Kalman filter movement estimation[J].SPIE,2005,5637:406-412.
    [8]BIEMOND J, JELLE R, GERBRANDS J J. A fast Kalman filter for images degraded by both blur and noise[J]. IEEE Trans. On ASSP, 1983,31:1248-1256.
    [9]WILSON T C.SHEPPAND J R. Theory and prac-tice of scanning optical microscopy[M]. London:Academic,1984.
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出版历程
  • 刊出日期:  2010-01-14

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