End-to-End Characterization of Colloidal Particles through Holographic Microscopy and Deep Convolutional Neural Networks
Analyzing holograms of colloidal particles with Lorenz-Mie theory yields the particles' sizes, refractive indexes and three-dimensional positions, all with exquisite precision and accuracy. No other technique provides such a wealth of particle-resolved and time-resolved characterization data. The underlying fits to Lorenz-Mie theory, however, require estimates for the particles' positions and properties that are good enough to ensure convergence to the optimal solution. Here, we demonstrate that this estimation problem can be solved with a single, specially structured deep convolutional neural network. The machine-learning approach to holographic particle characterization is orders of magnitude faster than conventional image-analysis techniques, substantially more robust against image defects, and yields answers that already are sufficiently precise for many applications. We demonstrate the method's efficacy through experimental measurements of the properties and dynamics of model colloidal systems.
APS March Meeting Abstracts
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