Statistical denoising scheme for single molecule fluorescence microscopic images

• Applying simple but robust de-noising algorithms to one of interesting biomedical applications.
• Designing the de-noising algorithms in a Bayesian framework.
• Introducing heterogeneous priors to Gaussian Markov random field (GMRF) to construct flexible and robust algorithms.

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.