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Single molecule fluorescence microscopy is a powerful technique foruncovering detailed information about biological systems, both in vitro and invivo. In such experiments, the inherently low signal to noise ratios mean thataccurate algorithms to separate true signal and background noise are essentialto generate meaningful results. To this end, we have developed a new and robustmethod to reduce noise in single molecule fluorescence images by using aGaussian Markov Random Field (GMRF) prior in a Bayesian framework. Twodifferent strategies are proposed to build the prior - an intrinsic GMRF, witha stationary relationship between pixels and a heterogeneous intrinsic GMRF,with a differently weighted relationship between pixels classified as moleculesand background. Testing with synthetic and real experimental fluorescenceimages demonstrates that the heterogeneous intrinsic GMRF is superior to otherconventional de-noising approaches.

Statistical Denoising for single molecule fluorescence microscopic images