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Proceedings Paper

ML-blind deconvolution algorithm: recent developments
Author(s): Santosh Bhattacharyya; Donald H. Szarowski; James N. Turner; Nathan J. O'Connor; Timothy J. Holmes
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Paper Abstract

The Maximum Likelihood based blind deconvolution (ML-blind) algorithm is used to deblur 3D microscope images. This approach was first introduced to the microscope community by us circa 1992. The basic advantage of a blind algorithm is that it simplifies the user interface protocols and reconstructs both the object and the Point Spread Function. In this paper we will discuss the recent improvements to the algorithm that robustize the performance and accelerate the speed of convergence. For instance, powerful and physically justified constraints are enforced on the reconstructed PSF at every iteration for robustization. A line search technique is added to the object reconstruction to accelerate the convergence of the object estimate. A simple modification to the algorithm enables adaptation for the transmitted light brightfield modality. Finally, we incorporate montaging in order to process large data fields.

Paper Details

Date Published: 10 April 1996
PDF: 12 pages
Proc. SPIE 2655, Three-Dimensional Microscopy: Image Acquisition and Processing III, (10 April 1996); doi: 10.1117/12.237475
Show Author Affiliations
Santosh Bhattacharyya, Rensselaer Polytechnic Institute (United States)
Donald H. Szarowski, New York State Dept. of Health (United States)
James N. Turner, Rensselaer Polytechnic Institute and New York State Dept. of Health (United States)
Nathan J. O'Connor, Rensselaer Polytechnic Institute and Autoquant Imaging, Inc. (United States)
Timothy J. Holmes, Rensselaer Polytechnic Institute and Autoquant Imaging, Inc. (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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