Motion-resolved quantitative phase imaging using spatiotemporal priors

Yunhui Gao; Zhengzhong Huang; Liangcai Cao

doi:10.1117/12.3000127

13 March 2024 Motion-resolved quantitative phase imaging using spatiotemporal priors

Yunhui Gao, Zhengzhong Huang, Liangcai Cao

Proceedings Volume PC12852, Quantitative Phase Imaging X; PC1285212 (2024) https://doi.org/10.1117/12.3000127
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

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 takes into account the spatiotemporal image priors. In particular, total variation with respect to the complex spatio-temporal datacube is introduced as a sparsity-promoting regularizer. The phase retrieval process is formulated as a standard optimization problem and is solved via an accelerated proximal gradient method. The algorithms are evaluated on a proof-of-concept QPI imaging system based on defocus diversity. Numerical and experimental results both indicate that the proposed spatio-temporal compressive phase retrieval framework could achieve high-fidelity quantitative phase imaging while improving the temporal resolution to that of a single-shot method. We experimentally demonstrate video-rate QPI of dynamic biological activities that is free of motion blur and twin-image artifacts. The proposed framework could potentially achieve a high space-bandwidth-time product and push the information throughput of QPI systems towards the theoretic limit.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunhui Gao, Zhengzhong Huang, and Liangcai Cao "Motion-resolved quantitative phase imaging using spatiotemporal priors", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC1285212 (13 March 2024); https://doi.org/10.1117/12.3000127

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