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Bayesian-based deconvolution fluorescence microscopy using dynamically updated nonstationary expectation estimates
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DOI：10.1038/srep10849.png)
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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.
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IMAGE-RESTORATION
REGULARIZATION
ALGORITHM
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3.9
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27.9W
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83.5W
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