@article {6962,
	title = {Efficient Bayesian-based multiview deconvolution.},
	journal = {Nature Methods},
	volume = {11},
	year = {2014},
	month = {2014 Apr 20},
	pages = {645-8},
	abstract = {<p>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.</p>
},
	issn = {1548-7105},
	doi = {10.1038/nmeth.2929},
	author = {Preibisch, Stephan and Amat, Fernando and Stamataki, Evangelia and Sarov, Mihail and Singer, Robert H and Myers, Eugene and Tomancak, Pavel}
}
