ArticleScientific reports2015
Bayesian-based deconvolution fluorescence microscopy using dynamically updated nonstationary expectation estimates.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring.Optics express · 2018Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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