Existing quantitative phase imaging (QPI) techniques are faced with an inherent trade-off between phase imaging fidelity and temporal resolution. Here, we propose a general algorithmic framework for QPI reconstruction that enables frame-rate-limited holographic imaging. It takes an inverse problem approach by formulating phase retrieval as a nonsmooth nonconvex optimization problem. Efficient solvers for the problem are derived whose algorithmic behaviors have been studied from both theoretical and experimental perspectives. The proposed framework is applicable to various existing holographic imaging configurations, and makes it possible to incorporate advanced image priors for quality enhancement.

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Yunhui Gao and Liangcai Cao, "High-throughput quantitative phase imaging via compressive phase retrieval," Proc. SPIE 12389, Quantitative Phase Imaging IX, 123890C (Presented at SPIE BiOS: January 30, 2023; Published: 16 March 2023); https://doi.org/10.1117/12.2655445.