Statistical Denoising for single molecule fluorescence microscopic images

TL;DR

This paper introduces a novel Bayesian denoising method for single molecule fluorescence microscopy images using Gaussian Markov Random Field priors, significantly improving noise reduction in low signal-to-noise conditions.

Contribution

It proposes a new heterogeneous intrinsic GMRF prior within a Bayesian framework, enhancing noise removal over traditional methods in fluorescence microscopy.

Findings

Heterogeneous intrinsic GMRF outperforms conventional denoising techniques.

The method effectively reduces noise in both synthetic and real images.

Improves the quality of biological data analysis from fluorescence microscopy.

Abstract

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.