Structured illumination-based phase retrieval via Generative Adversarial Network

Ziling Wu; Xiaofeng Wu; Yunhui Zhu

doi:10.1117/12.2547551

14 February 2020 Structured illumination-based phase retrieval via Generative Adversarial Network

Ziling Wu, Xiaofeng Wu, Yunhui Zhu

Proceedings Volume 11249, Quantitative Phase Imaging VI; 112490L (2020) https://doi.org/10.1117/12.2547551
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Ziling Wu, Xiaofeng Wu, and Yunhui Zhu "Structured illumination-based phase retrieval via Generative Adversarial Network", Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490L (14 February 2020); https://doi.org/10.1117/12.2547551

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