Fast regularization technique for expectation maximization algorithm for optical sectioning microscopy

Jose-Angel Conchello; James G. McNally

doi:10.1117/12.237477

10 April 1996 Fast regularization technique for expectation maximization algorithm for optical sectioning microscopy

Jose-Angel Conchello, James G. McNally

Proceedings Volume 2655, Three-Dimensional Microscopy: Image Acquisition and Processing III; (1996) https://doi.org/10.1117/12.237477
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

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.

Citation Download Citation

Jose-Angel Conchello and James G. McNally "Fast regularization technique for expectation maximization algorithm for optical sectioning microscopy", Proc. SPIE 2655, Three-Dimensional Microscopy: Image Acquisition and Processing III, (10 April 1996); https://doi.org/10.1117/12.237477

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