title: Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy.
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.
authors.affiliation: Department of Electrical and Systems Engineering, Washington University, St. Louis, Missouri 63130, USA.
authors.forename: Chrysanthe
authors.initials: C
authors.lastname: Preza
authors.forename: José-Angel
authors.initials: JA
authors.lastname: Conchello
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008856:Microscopy, Fluorescence; D008962:Models, Theoretical