Experiment-data-free self-supervised neural network for holographic microscopy imaging

Luzhe Huang; Hanlong Chen; Tairan Liu; Aydogan Ozcan

doi:10.1117/12.3000665

12 March 2024 Experiment-data-free self-supervised neural network for holographic microscopy imaging

Luzhe Huang, Hanlong Chen, Tairan Liu, Aydogan Ozcan

Proceedings Volume 12852, Quantitative Phase Imaging X; 128520B (2024) https://doi.org/10.1117/12.3000665
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

We introduce GedankenNet, a self-supervised learning model for hologram reconstruction. During its training, GedankenNet leveraged a physics-consistency loss informed by the physical forward model of the imaging process, and simulated holograms generated from artificial random images with no correspondence to real-world samples. After this experimental-data-free training based on “Gedanken Experiments”, GedankenNet successfully generalized to experimental holograms on its first exposure to real-world experimental data, reconstructing complex fields of various samples. This self-supervised learning framework based on a physics-consistency loss and Gedanken experiments represents a significant step toward developing generalizable, robust and physics-driven AI models in computational microscopy and imaging.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Luzhe Huang, Hanlong Chen, Tairan Liu, and Aydogan Ozcan "Experiment-data-free self-supervised neural network for holographic microscopy imaging", Proc. SPIE 12852, Quantitative Phase Imaging X, 128520B (12 March 2024); https://doi.org/10.1117/12.3000665

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