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Noise suppression of point spread functions and its influence on deconvolution of three‐dimensional fluorescence microscopy image sets
Author(s) -
LAI X.,
LIN ZHIPING,
WARD E. S.,
OBER R. J.
Publication year - 2005
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.0022-2720.2005.01440.x
Subject(s) - deconvolution , point spread function , image restoration , noise (video) , blind deconvolution , noise reduction , algorithm , wiener deconvolution , image (mathematics) , computer science , microscopy , point (geometry) , image processing , artificial intelligence , optics , mathematics , physics , geometry
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.