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Bayesian-based deconvolution fluorescence microscopy using dynamically updated nonstationary expectation estimates
- Source :
- Scientific Reports
- Publication Year :
- 2015
- Publisher :
- Springer Science and Business Media LLC, 2015.
-
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.
- Subjects :
- Blind deconvolution
0303 health sciences
Multidisciplinary
business.industry
Computer science
Image quality
Bayesian probability
Pattern recognition
computer.software_genre
01 natural sciences
Regularization (mathematics)
Article
010309 optics
03 medical and health sciences
Biological specimen
0103 physical sciences
Fluorescence microscope
Artificial intelligence
Deconvolution
Data mining
business
computer
030304 developmental biology
Subjects
Details
- ISSN :
- 20452322
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....6e71e76cd2f956b753bc2a00b93e8388