Precise measurements in digital holographic microscopy by modeling the optical train.

In the past few years, the venerable field of holographic microscopy has been revitalized by computational data analysis. It is now possible to fit a generative (forward) model of scattering directly to experimentally obtained holograms of complex microscopic objects. This approach enables precision measurements: it allows the motion of colloidal particles and biological organisms to be tracked with nanometer-scale precision and their optical properties to be inferred particle by particle. In this Perspective, we discuss how the model-based inference approach to holographic microscopy has opened up new applications. We also discuss how it must evolve to meet the needs of emerging applications that demand lower systematic uncertainties and higher precision. In this context, we present some new results on how modeling the optical train of the microscope can enable better measurements of the positions of spherical and nonspherical colloidal particles. Finally, we discuss how machine learning might play a role in future advances. Though we do not exhaustively catalog all the developments in this field, we show a few examples and some new results that spotlight open questions and opportunities. [ABSTRACT FROM AUTHOR]