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Proceedings Paper

Fast regularization technique for expectation maximization algorithm for optical sectioning microscopy
Author(s): Jose-Angel Conchello; James G. McNally
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Paper Abstract

Maximum likelihood image restoration is a powerful method for 3D computational optical sectioning microscopy of extended objects. With punctate specimens, however, this method produces a few very bright isolated spots and dim detail around them is lost. The commonly used regularization methods (sieves and roughness penalty) decrease the amplitude of the bright spots, but do not avoid loosing dim detail. We derived an intensity regularization that decreases the amplitude of bright spots without loosing dim detail. In contrast with other regularization methods, this method does not increase significantly the computational complexity of the estimation algorithm.

Paper Details

Date Published: 10 April 1996
PDF: 10 pages
Proc. SPIE 2655, Three-Dimensional Microscopy: Image Acquisition and Processing III, (10 April 1996); doi: 10.1117/12.237477
Show Author Affiliations
Jose-Angel Conchello, Washington Univ. (United States)
James G. McNally, Washington Univ. (United States)


Published in SPIE Proceedings Vol. 2655:
Three-Dimensional Microscopy: Image Acquisition and Processing III
Carol J. Cogswell; Gordon S. Kino; Tony Wilson, Editor(s)

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