Abstract
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.
Highlights
Noise suppression[25,26,27,28]
To evaluate the proposed MAP method with dynamically updated nonstationary expectation estimates, we first simulated a fluorescence microscopy data set with fluorescence-stained cell populations using a modified version of SIMCEP30, a computational framework for simulating fluorescence microscopy images of fluorescence-stained cell populations
Note that LR, MAP-Hunt, and the proposed MAP-D methods are performed without the explicit use of spatial regularization to test the hypothesis that MAP-D can achieve improved fluorescence microscopy image quality in the presence of noise, without explicit use of spatial regularization
Summary
Noise suppression[25,26,27,28]. In this study, we investigate and attempt to mitigate this issue from a different perspective by introducing an MAP method that performs deconvolution on fluorescence microscopy images using dynamically updated nonstationary expectation estimates, which does not make explicit use of spatial regularization. In previous MAP methods[10], the measured fluorescence microscopy image g was used as an estimate of E(fs), based on the assumption that the measured image, having undergone the influence of the PSF, is similar to a nonstationary local average and representative of a nonstationary expectation E(fs). This assumption may not be reliable in high noise situations under low SNR imaging conditions, as well as result in high-varying estimates depending on the inherent PSF. The importance of introducing a dynamically updated estimate koefrEne(lf-sb)asuenddeerstitmheatienfoluf eEn(cfes
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