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)


   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},




   keywords={Quantitative Biology – Quantitative Methods},




   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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2021 hgpu.org

All rights belong to the respective authors

Contact us: