@article {836,
	title = {Bayesian-based deconvolution fluorescence microscopy using dynamically updated nonparametric nonstationary expectation estimates},
	journal = {Nature Scientific Reports},
	year = {2015},
	abstract = {<p>Fluorescence microscopy is widely used for the study of biological specimens. Deconvolution can significantly improve the resolution and contrast of images produced using fluorescence microscopy; in particular, Bayesian-based methods have become very popular in deconvolution fluorescence microscopy. An ongoing challenge with Bayesian-based methods is in dealing with the presence of noise in low SNR imaging conditions. In this study, we present a Bayesian-based method for performing deconvolution using dynamically updated nonstationary expectation estimates that can improve the fluorescence microscopy image quality in the presence of noise, without explicit use of spatial regularization.</p>
},
	doi = {http://dx.doi.org/10.1038\%2Fsrep10849},
	author = {A Wong and X Wang and M Gorbet}
}
