A two-branch deep neural network using lateral and longitudinal diffraction features achieves high extraction rates and speeds for holographic 3D particle imaging, with broad generalizability claimed but only partially validated.
Quantitative measurement of size and three-dimensional position of fast-moving bubbles in air-water mixture flows using digital holography,
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2024 1verdicts
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Generalizable Deep Learning Approach for 3D Particle Imaging using Holographic Microscopy
A two-branch deep neural network using lateral and longitudinal diffraction features achieves high extraction rates and speeds for holographic 3D particle imaging, with broad generalizability claimed but only partially validated.