Statistical denoising scheme for single molecule fluorescence microscopic images

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5 Citations (Scopus)


Single molecule fluorescence microscopy is a powerful technique for uncovering detailed information about biological systems, both in vitro and in vivo. In such experiments, the inherently low signal to noise ratios mean that accurate algorithms to separate true signal and background noise are essential to generate meaningful results. To this end, we have developed a new and robust method to reduce noise in single molecule fluorescence images by using a Gaussian Markov random field (GMRF) prior in a Bayesian framework. Two different strategies are proposed to build the prior - an intrinsic GMRF, with a stationary relationship between pixels and a heterogeneous intrinsic GMRF, with a differently weighted relationship between pixels classified as molecules and background. Testing with synthetic and real experimental fluorescence images demonstrates that the heterogeneous intrinsic GMRF is superior to other conventional de-noising approaches.

Original languageEnglish
Pages (from-to)11-20
Number of pages10
JournalBiomedical Signal Processing and Control
Issue number1
Publication statusPublished - 2014 Mar

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A1012797). We also thank for P.D. Dunne and D. Klenerman at Cambridge University for providing experimental data sets.


  • Adaptive prior
  • Bayesian
  • De-noising

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics


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