A physics-informed neural network trained self-supervised with phase diversity reconstructs quantitative transmission functions from single in-line holograms with accuracy matching or exceeding regularized inversion at 1000x lower compute cost.
Real-time 3-d sensing, visualization and recognition of dynamic biological microorganisms,
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2026 1verdicts
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Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction
A physics-informed neural network trained self-supervised with phase diversity reconstructs quantitative transmission functions from single in-line holograms with accuracy matching or exceeding regularized inversion at 1000x lower compute cost.