Hierarchical synthesis network: a frequency extraction technique for structured illumination-based phase retrieval

Xiaofeng Wu; Ziling Wu; Sibi Chakravarthy Shanmugavel; Yunhui Zhu

doi:10.1117/12.2584563

5 March 2021 Hierarchical synthesis network: a frequency extraction technique for structured illumination-based phase retrieval

Xiaofeng Wu, Ziling Wu, Sibi Chakravarthy Shanmugavel, Yunhui Zhu

Author Affiliations +

Proceedings Volume 11653, Quantitative Phase Imaging VII; 116530Y (2021) https://doi.org/10.1117/12.2584563
Event: SPIE BiOS, 2021, Online Only

Abstract

Structured illumination (SI) phase imaging is an important strategy to achieve quantitative phase imaging via encoding phase-induced diffraction into modulation intensity signals through propagation. However, the nonlinear property of SI-based transfer function results in ill-posedness in phase imaging retrieval. Overlapping modulation spectrum usually leads to loss of high spatial frequency components. Recent studies show that such nonlinear inversion problems can be efficiently represented by deep neural networks, as have been demonstrated in phase retrieval via holography and Fourier ptychography techniques. Here we present a hierarchical synthesis network (HSNet) which uses multiple splitting networks to extract structural features of structured intensity images in various modulation frequency and synthesis network to produce high fidelity reconstruction. We show that the proposed framework retrieve clear and accurate phase profile with reduced computing requirements in simulation.

Conference Presentation

Citation Download Citation

Xiaofeng Wu, Ziling Wu, Sibi Chakravarthy Shanmugavel, and Yunhui Zhu "Hierarchical synthesis network: a frequency extraction technique for structured illumination-based phase retrieval", Proc. SPIE 11653, Quantitative Phase Imaging VII, 116530Y (5 March 2021); https://doi.org/10.1117/12.2584563

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