Noise suppression of point spread functions and its influence on deconvolution of three-dimensional fluorescence microscopy image sets.
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.
Lai
X
X
Center for Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Lin
Zhiping
Z
Ward
E S
ES
Ober
R J
RJ
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019169:Jurkat Cells; D008856:Microscopy, Fluorescence