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Proceedings Paper

Structured illumination-based phase retrieval via Generative Adversarial Network
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Paper Abstract

Structured-illumination (SI) is used for quantitative phase retrieval for improved contrast and sensitivity. However, the nonlinear nature of SI-based phase retrieval process, such as the spatial frequency biases and mixture of different spatial frequency components, usually leads to phase aberrations, in particular in the high spatial frequency components. Recent studies show that nonlinear inversion problems can be efficiently represented by deep neural networks in an end-to-end framework. In this study, we present a deep learning framework for SI-based quantitative phase imaging via the Conditional Generative Adversarial Network (cGANs). A series of structured images paired with the corresponding ground truth of phase images are used to train two competing networks of generator and discriminator. We demonstrate that the GAN-based approach produces sharp and accurate phase image and the structured illumination pattern simultaneously based on our simulation.

Paper Details

Date Published: 14 February 2020
PDF: 9 pages
Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490L (14 February 2020); doi: 10.1117/12.2547551
Show Author Affiliations
Ziling Wu, Virginia Polytechnic Institute and State Univ. (United States)
Xiaofeng Wu, Virginia Polytechnic Institute and State Univ. (United States)
Yunhui Zhu, Virginia Polytechnic Institute and State Univ. (United States)

Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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