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arXiv 2210.04033physics.opticseess.SP
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动态散射介质传输矩阵的在线学习:波前整形遇上多维时间序列

Online learning of the transfer matrix of dynamic scattering media: wavefront shaping meets multidimensional time series

Lorenzo Valzania, Sylvain Gigan

AI总结:

本文提出一种基于时间序列分析的递归在线优化方法,用于在任意长时间尺度上高效跟踪动态散射介质的传输矩阵,兼具优化例程与传输矩阵测量的优势,且内存占用低,可方便集成到现有波前整形装置中,为活体生物样本的光学研究开辟了新途径。

摘要:

得益于波前整形技术的最新进展,光学方法已被证明对于在多重散射介质(如生物组织)中实现成像和光控制至关重要。然而,活体生物样本的稳定时间往往使这些方法难以深入了解细胞和器官系统中的相关功能机制。在此,我们提出一种借鉴自时间序列分析的递归在线优化例程,用于在任意长时间尺度上最优地跟踪动态散射介质的传输矩阵。该方法在保留基于优化的例程和传输矩阵测量两者优势的同时,以内存高效的方式运行。由于它可以轻松地部署在现有的波前整形装置中——这些装置具备振幅和/或相位调制以及相位分辨或仅强度采集功能——它为活体生物样本的高效光学研究铺平了道路。

英文摘要:

Thanks to the latest advancements in wavefront shaping, optical methods have proven crucial to achieve imaging and control light in multiply scattering media, like biological tissues. However, the stability times of living biological specimens often prevent such methods from gaining insights into relevant functioning mechanisms in cellular and organ systems. Here we present a recursive and online optimization routine, borrowed from time series analysis, to optimally track the transfer matrix of dynamic scattering media over arbitrarily long timescales. While preserving the advantages of both optimization-based routines and transfer-matrix measurements, it operates in a memory-efficient manner. Because it can be readily implemented in existing wavefront shaping setups, featuring amplitude and/or phase modulation and phase-resolved or intensity-only acquisition, it paves the way for efficient optical investigations of living biological specimens.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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