Efficient bayesian multi-view deconvolution
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.
August 6, 2013 by hgpu
