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On the use of deep learning for phase recovery

Light Science & Applications 2024 173 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 65 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Kaiqiang Wang, Kaiqiang Wang, Li Song, Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Chutian Wang, Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Chutian Wang, Zhenbo Ren, Zhenbo Ren, Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Guangyuan Zhao, Guangyuan Zhao, Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Edmund Y. Lam Jiazhen Dou, Jianglei Di, Edmund Y. Lam George Barbastathis, George Barbastathis, Renjie Zhou, Jianlin Zhao, Edmund Y. Lam

Summary

Researchers reviewed how deep learning — a type of artificial intelligence — can recover phase information from light, which is typically lost when cameras capture images, enabling sharper microscopy and better materials analysis. These advances improve the tools scientists use to study tiny particles, including microplastics, at very fine scales.

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

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