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.
Tutorial: Aerosol characterization with digital in-line holography,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.optics 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.