Noise suppression of point spread functions and its influence on deconvolution of three-dimensional fluorescence microscopy image sets

Authors

  • X. LAI,

    1. Center for Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390, U.S.A.
    2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S2, Nanyang Avenue, Singapore 639798, Republic of Singapore
    3. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75083, U.S.A.
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  • ZHIPING LIN,

    1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S2, Nanyang Avenue, Singapore 639798, Republic of Singapore
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  • E. S. WARD,

    1. Center for Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390, U.S.A.
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  • R. J. OBER

    Corresponding author
    1. Center for Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390, U.S.A.
    2. Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75083, U.S.A.
      Raimund J. Ober. Fax: +1 214 648 1259; e-mail: ober@utdallas.edu.
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Raimund J. Ober. Fax: +1 214 648 1259; e-mail: ober@utdallas.edu.

Summary

The point spread function (PSF) is of central importance in the image restoration of three-dimensional image sets acquired by an epifluorescent microscope. Even though it is well known that an experimental PSF is typically more accurate than a theoretical one, the noise content of the experimental PSF is often an obstacle to its use in deconvolution algorithms. In this paper we apply a recently introduced noise suppression method to achieve an effective noise reduction in experimental PSFs. We show with both simulated and experimental three-dimensional image sets that a PSF that is smoothed with this method leads to a significant improvement in the performance of deconvolution algorithms, such as the regularized least-squares algorithm and the accelerated Richardson–Lucy algorithm.

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