Efficient Bayesian-based Multi-View Deconvolution
Quantitative Methods
2014-12-03 v3
Abstract
Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the sam- ples from multiple angles. Multi-view deconvolution can significantly improve the resolution and contrast of the images, but its application has been limited due to the large size of the datasets. Here we present a Bayesian- based derivation of multi-view deconvolution that drastically improves the convergence time and provide a fast implementation utilizing graphics hardware.
Keywords
Cite
@article{arxiv.1308.0730,
title = {Efficient Bayesian-based Multi-View Deconvolution},
author = {Stephan Preibisch and Fernando Amat and Evangelia Stamataki and Mihail Sarov and Robert H. Singer and Eugene Myers and Pavel Tomancak},
journal= {arXiv preprint arXiv:1308.0730},
year = {2014}
}
Comments
48 pages, 20 figures, 1 table, under review at Nature Methods