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Proceedings Paper

Tradeoffs in regularized maximum-likelihood image restoration
Author(s): Joanne Markham; Jose-Angel Conchello
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Paper Abstract

All algorithms for three-dimensional deconvolution of fluorescence microscopical images have as a common goal the estimation of a specimen function (SF) that is consistent with the recorded image and the process for image formation and recording. To check for consistency, the image of the estimated SF predicted by the imaging operator is compared to the recorded image, and the similarity between them is used as a figure of merit (FOM) in the algorithm to improve the specimen function estimate. Commonly used FOMs include squared differences, maximum entropy, and maximum likelihood (ML). The imaging operator is usually characterized by the point-spread function (PSF), the image of a point source of light, or its Fourier transform, the optical transfer function (OTF). Because the OTF is non-zero only over a small region of the spatial-frequency domain, the inversion of the image formation operator is non-unique and the estimated SF is potentially artifactual. Adding a term to the FOM that penalizes some unwanted behavior of the estimated SF effectively ameliorates potential artifacts, but at the same time biases the estimation process. For example, an intensity penalty avoids overly large pixel values but biases the SF to small pixel values. A roughness penalty avoids rapid pixel to pixel variations but biases the SF to be smooth. In this article we assess the effects of the roughness and intensity penalties on maximum likelihood image estimation.

Paper Details

Date Published: 10 April 1997
PDF: 10 pages
Proc. SPIE 2984, Three-Dimensional Microscopy: Image Acquisition and Processing IV, (10 April 1997); doi: 10.1117/12.271259
Show Author Affiliations
Joanne Markham, Washington Univ. (United States)
Jose-Angel Conchello, Washington Univ. (United States)

Published in SPIE Proceedings Vol. 2984:
Three-Dimensional Microscopy: Image Acquisition and Processing IV
Carol J. Cogswell; Jose-Angel Conchello; Tony Wilson, Editor(s)

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