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Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy.

Chrysanthe Preza·José-Angel Conchello
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Abstract

We derive an algorithm for maximum-likelihood image estimation on the basis of the expectation-maximization (EM) formalism by using a new approximate model for depth-varying image formation for optical sectioning microscopy. This new strata-based model incorporates spherical aberration that worsens as the microscope is focused deeper under the cover slip and is the result of the refractive-index mismatch between the immersion medium and the mounting medium of the specimen. Images of a specimen with known geometry and refractive index show that the model captures the main features of the image. We analyze the performance of the depth-variant EM algorithm with simulations, which show that the algorithm can compensate for image degradation changing with depth.

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AlgorithmsComputer SimulationImage Processing, Computer-AssistedImaging, Three-DimensionalMicroscopy, FluorescenceModels, TheoreticalEvaluation StudyJournal ArticleResearch Support, U.S. Gov't, P.H.S.

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BibTeXRIS

Chrysanthe Preza, José-Angel Conchello. 2004. Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy.. https://doi.org/10.1364/josaa.21.001593

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