Generative Models for Digital Holographic Microscopy
Alexander, Ronald D.
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CitationAlexander, Ronald D. 2020. Generative Models for Digital Holographic Microscopy. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractIn 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. 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 inferred on a particle-by-particle basis. In this thesis, I discuss how the model-based inference approach to holographic microscopy is opening up new applications. I also discuss how it must evolve to meet the needs of new applications that demand lower systematic uncertainties and maximum precision. In this context, I present some new and previous results on how modeling the optical train of the microscope can enable better measurements of the positions of spherical and nonspherical colloidal particles. Finally, I discuss how machine learning might play a role in future advances. Though I do not exhaustively catalogue all the developments in this field, I hope that by presenting a few examples and some new results I can spotlight open questions and opportunities.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365842
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