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

J Microsc. 2005 Jan;217(Pt 1):93-108. doi: 10.1111/j.0022-2720.2005.01440.x.


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.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Humans
  • Image Processing, Computer-Assisted*
  • Jurkat Cells
  • Microscopy, Fluorescence*