All-optical quantitative phase imaging through random diffusers using a diffractive network

Yuhang Li; Yi Luo; Deniz Mengu; Bijie Bai; Aydogan Ozcan

doi:10.1117/12.2678212

28 September 2023 All-optical quantitative phase imaging through random diffusers using a diffractive network

Yuhang Li, Yi Luo, Deniz Mengu, Bijie Bai, Aydogan Ozcan

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550T (2023) https://doi.org/10.1117/12.2678212
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

We present a novel approach to perform quantitative phase imaging (QPI) through random phase diffusers using a diffractive neural network consisting of successive diffractive layers optimized using deep learning. This diffractive network is trained to convert the phase information of samples positioned behind random diffusers into intensity variations at the output, enabling all-optical phase recovery and quantitative phase imaging of objects hidden by unknown random diffusers. Unlike traditional digital image reconstruction methods, our all-optical diffractive processor does not require external power beyond the illumination beam and operates at the speed of light propagation.

Conference Presentation

Citation Download Citation

Yuhang Li, Yi Luo, Deniz Mengu, Bijie Bai, and Aydogan Ozcan "All-optical quantitative phase imaging through random diffusers using a diffractive network", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550T (28 September 2023); https://doi.org/10.1117/12.2678212

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