Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri 63130, USA.
Published: September 2004
AI Article Synopsis
Article 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.
Information and Communication Engineering, Graduate School of Information Science and Technology, the University of Tokyo, Hongo, Bunkyo, Tokyo, 113-8656, Japan.
The 3D wide-field fluorescence microscopy suffers from depth-variant asymmetric blur. The depth-variance and axial asymmetry are due to refractive index mismatch between the immersion and the specimen layer. The radial asymmetry is due to lens imperfections and local refractive index inhomogeneities in the specimen.
We investigated the deconvolution of 3D widefield fluorescence microscopy using the penalized maximum likelihood estimation method and the depth-variant point spread function (DV-PSF). We build the DV-PSF by fitting a parameterized theoretical PSF model to an experimental microbead image. On the basis of the constructed DV-PSF, we restore the 3D widefield microscopy by minimizing an objective function consisting of a negative Poisson likelihood function and a total variation regularization function.
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.
