Journal ArticleDOI
Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy
TLDR
An algorithm for maximum-likelihood image estimation on the basis of the expectation-maximization (EM) formalism is derived by using a new approximate model for depth-varying image formation for optical sectioning microscopy that incorporates spherical aberration that worsens as the microscope is focused deeper under the cover slip.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.read more
Citations
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3‐D PSF fitting for fluorescence microscopy: implementation and localization application
TL;DR: A least‐squares point spread function fitting framework that utilizes the Gibson and Lanni model and a computationally efficient way for evaluating its derivative functions is introduced and demonstrated with algorithms for particle localization and defocus estimation.
Journal ArticleDOI
The colored revolution of bioimaging
TL;DR: An overview of the main aspects of modern fluorescence microscopy is provided, which covers the principles of fluorescence and highlights the key discoveries in the history of fluorescent microscopy.
Journal ArticleDOI
A maximum-likelihood formalism for sub-resolution axial localization of fluorescent nanoparticles
TL;DR: A new approach for axial localization is proposed: it consists of a maximum-likelihood estimator based on a theoretical image formation model that incorporates noise, and the fundamental theoretical limits on localization are studied.
Journal ArticleDOI
Fast Approximations of Shift-Variant Blur
TL;DR: Comparison of theoretical properties and empirical performances of each blur approximation suggests that the proposed general model is preferable for approximation and inversion of a known shift-variant blur.
Journal ArticleDOI
Primal-Dual Decomposition by Operator Splitting and Applications to Image Deblurring ∗
TL;DR: Numerical results indicate that the primal-dual splitting methods compare favorably with the alternating direction method of multipliers, the Douglas--Rachford algorithm applied to a reformulated primal problem, and the Chambolle--Pock primal- DUal algorithm.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Random Point Processes in Time and Space
TL;DR: This senior graduate level textbook is the second revised edition of the textbook "Random Point Processes", written by D.L.Snyder and published in 1975, reflecting the increase that has taken place in the use of point-process models for the description of data from which images of objects of interest are formed in a wide variety of scientific and engineering disciplines.
Journal ArticleDOI
Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems
TL;DR: In this article, logically consistent rules for selecting a vector from any feasible set defined by linear constraints, when either all $n$-vectors or those with positive components or the probability vectors are permissible, are determined.
Journal ArticleDOI
Optical sectioning microscopy.
TL;DR: The core concepts of confocal microscopes and important variables that adversely affect confocal images are described and computational optical sectioning techniques that can perform Optical sectioning without a confocal microscope are discussed.