Deep learning-based hologram reconstruction with superior external generalization (Conference Presentation)

Hanlong Chen; Luzhe Huang; Tairan Liu; Aydogan Ozcan

doi:10.1117/12.2648180

16 March 2023 Deep learning-based hologram reconstruction with superior external generalization (Conference Presentation)

Hanlong Chen, Luzhe Huang, Tairan Liu, Aydogan Ozcan

Author Affiliations +

Proceedings Volume PC12389, Quantitative Phase Imaging IX; PC123890P (2023) https://doi.org/10.1117/12.2648180
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

We demonstrate a deep learning-based framework, called Fourier Imager Network (FIN), which achieves unparalleled generalization in end-to-end phase-recovery and hologram reconstruction. We used Fourier transform modules in FIN architecture, which process the spatial frequencies of the input images in a global receptive field and bring strong regularization and robustness to the hologram reconstruction task. We validated FIN by training it on human lung tissue samples and blindly testing it on human prostate, salivary gland, and Pap smear samples. FIN exhibits superior internal and external generalization compared with existing hologram reconstruction models, also achieving a ~50-fold acceleration in image inference speed.

Conference Presentation

Citation Download Citation

Hanlong Chen, Luzhe Huang, Tairan Liu, and Aydogan Ozcan "Deep learning-based hologram reconstruction with superior external generalization (Conference Presentation)", Proc. SPIE PC12389, Quantitative Phase Imaging IX, PC123890P (16 March 2023); https://doi.org/10.1117/12.2648180

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