Markov Chain Monte Carlo Super-resolution Image Reconstruction With Artifacts Suppression

2006 ◽  
Author(s):  
Jing Tian ◽  
Kai-Kuang Ma
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Resolution Image

scholarly journals Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

2019 ◽  
Author(s):  
Mohamadreza Fazel ◽  
Michael J. Wester ◽  
Hanieh Mazloom-Farsibaf ◽  
Marjolein B. M. Meddens ◽  
Alexandra Eklund ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Single Molecule ◽  
Posterior Probability ◽  
Super Resolution ◽  
Image Data ◽  
Reversible Jump ◽  
Posterior Probability Distribution ◽  
Resolution Imaging

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

scholarly journals Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mohamadreza Fazel ◽  
Michael J. Wester ◽  
Hanieh Mazloom-Farsibaf ◽  
Marjolein B. M. Meddens ◽  
Alexandra S. Eklund ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Single Molecule ◽  
Posterior Probability ◽  
Super Resolution ◽  
Image Data ◽  
Reversible Jump ◽  
Posterior Probability Distribution ◽  
Resolution Imaging

Abstract In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

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