Efficient Bayesian-based multiview deconvolution

@article{Preibisch2014EfficientBM,
  title={Efficient Bayesian-based multiview deconvolution},
  author={Stephan Preibisch and Fernando Amat and Evangelia Stamataki and Mihail Sarov and Robert H Singer and Eugene Myers and Pavel Tomançak},
  journal={Nature Methods},
  year={2014},
  volume={11},
  pages={645-648}
}
  • Stephan Preibisch, Fernando Amat, +4 authors Pavel Tomançak
  • Published in Nature Methods 2014
  • Biology, Medicine
  • 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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