10244

Efficient bayesian multi-view deconvolution

Stephan Preibisch, Fernando Amat, Evangelia Stamataki, Mihail Sarov, Eugene Myers, Pavel Tomancak
Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany
arXiv:1308.0730 [q-bio.QM], (3 Aug 2013)

@article{2013arXiv1308.0730P,

   author={Preibisch}, S. and {Amat}, F. and {Stamataki}, E. and {Sarov}, M. and {Myers}, E. and {Tomancak}, P.},

   title={"{Efficient bayesian multi-view deconvolution}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1308.0730},

   primaryClass={"q-bio.QM"},

   keywords={Quantitative Biology – Quantitative Methods},

   year={2013},

   month={aug},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1308.0730P},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the samples 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 derivation of multi-view Bayesian deconvolution that drastically improves the convergence time and provide a GPU implementation that optimizes runtime performance.
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