首页|相位恢复波前重构技术的发展与应用(特邀)

相位恢复波前重构技术的发展与应用(特邀)

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光波复振幅中相位信息的恢复是科学与工程领域的重要研究热点之一。相位携带了光传播中的重要信息,对成像与智能感知技术的发展有着重要的意义。相位恢复波前重构技术通过优化算法和设计特定成像装置,从光电探测器采集的强度信息中恢复出难以被直接感知的相位信息,是探测微观和宏观世界的重要技术手段之一,已广泛应用于生物显微、工业检测和天文观测等领域。概述基于干涉和非干涉的波前重构技术及其应用,梳理相位恢复波前重构算法的基本原理和发展历程,对常见相位恢复技术手段如交替投影相位恢复算法、基于调制约束和基于深度学习的相位恢复波前重构技术等进行初步的探讨。针对相位恢复波前重构技术的未来发展提出若干可能的研究方向,包括相位恢复算法的进一步优化、新型系统和器件的开发等。
Development and Application of Phase Retrieval Wavefront Reconstruction Techniques(Invited)
The retrieval of phase information from the complex amplitude of light waves is a pivotal area of research in various scientific and engineering fields,as phase carries essential information about the propagation of light,profoundly influencing the advancement of imaging and intelligent sensing technologies.Phase recovery wavefront reconstruction techniques,leveraging advanced optimization algorithms and specialized imaging setup,extract the challenging-to-detect phase information from intensity data gathered by imaging sensors.Phase recovery wavefront reconstruction technique is one of the key methods enabling the exploration of both micro and macro worlds,extensively applied in diverse fields such as biological microscopy,industrial inspection,and astronomical observation.This paper begins by outlining both interferometric and non-interferometric wavefront reconstruction techniques and their respective applications.Subsequently,it reviews the fundamental principles and developmental trajectory of phase retrieval algorithms in wavefront reconstruction.This encompasses a preliminary exploration of prevalent phase retrieval methodologies,including alternating projection phase retrieval algorithms,ptychography imaging,and phase retrieval wavefront reconstruction techniques integrating modulation constraints and deep learning.The article concludes by summarizing the entire discourse and outlining prospective directions for the future development of phase retrieval wavefront reconstruction technology,which include the enhancement of algorithms and the development of imaging systems and devices.

phase retrievalwavefront reconstructioncomputational imagingdeep learning

魏金文、李儒佳、吴佳琛、张启航、高云晖、曹良才

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清华大学精密仪器系,北京 100084

相位恢复 波前重构 计算成像 深度学习

2024

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子:1.153
ISSN:1006-4125
年,卷(期):2024.61(2)
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