Structured illumination microscopy with noise-controlled image reconstructions

Super-resolution structured illumination microscopy (SIM) has become a widely used method for biological imaging. Standard reconstruction algorithms, however, are prone to generate noise-specific artifacts that limit their applicability for lower signal-to-noise data. Here we present a physically realistic noise model that explains the structured noise artifact, which we then use to motivate new complementary reconstruction approaches. True-Wiener-filtered SIM optimizes contrast given the available signal-to-noise ratio, and flat-noise SIM fully overcomes the structured noise artifact while maintaining resolving power. Both methods eliminate ad hoc user-adjustable reconstruction parameters in favor of physical parameters, enhancing objectivity. The new reconstructions point to a trade-off between contrast and a natural noise appearance. This trade-off can be partly overcome by further notch filtering but at the expense of a decrease in signal-to-noise ratio. The benefits of the proposed approaches are demonstrated on focal adhesion and tubulin samples in two and three dimensions, and on nanofabricated fluorescent test patterns. Super-resolution structured illumination microscopy reconstruction algorithms are described that can handle structured noise artifacts in two and three dimensions. The algorithms lack adjustable parameters and enhance objective representation of imaged objects.

Automated summary

This study presents a physically realistic noise model explaining structured noise artifacts and proposes new complementary reconstruction approaches to eliminate them. By utilizing True-Wiener-filtered SIM and flat-noise SIM methods, contrast optimization and resolution maintenance are achieved while overcoming structured noise artifacts. These techniques enhance the objective representation of imaged objects without the need for adjustable parameters.