  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 nonparametric
nonstationary expectation estimates that can improve the fluorescence
microscopy image quality in the presence of noise, without explicit use of
spatial regularization.
