Efficient Bayesian-based multiview deconvolution
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Updated: May 1, 2026

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
Published on: April 10, 2016
Stephan Preibisch1, Fernando Amat2, Evangelia Stamataki3
11] Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. [2] Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. [3] Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York, USA. [4] Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, USA.
Abstract:
Light-sheet fluorescence microscopy is able to image large specimens with high resolution by capturing the samples from multiple angles. Multiview deconvolution can substantially improve the resolution and contrast of the images, but its application has been limited owing to the large size of the data sets. Here we present a Bayesian-based derivation of multiview deconvolution that drastically improves the convergence time, and we provide a fast implementation using graphics hardware.
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