首页|SSL Depth:self-supervised learning enables 16× speedup in confocal microscopy-based 3D surface imaging[Invited]

SSL Depth:self-supervised learning enables 16× speedup in confocal microscopy-based 3D surface imaging[Invited]

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In scientific and industrial research,three-dimensional[3D]imaging,or depth measurement,is a critical tool that provides detailed insight into surface properties.Confocal microscopy,known for its precision in surface measurements,plays a key role in this field.However,3D imaging based on confocal microscopy is often challenged by significant data requirements and slow measurement speeds.In this paper,we present a novel self-supervised learning algorithm called SSL Depth that overcomes these challenges.Specifically,our method exploits the feature learning capabilities of neural networks while avoiding the need for labeled data sets typically associated with supervised learning approaches.Through practical dem-onstrations on a commercially available confocal microscope,we find that our method not only maintains higher quality,but also significantly reduces the frequency of the Z-axis sampling required for 3D imaging.This reduction results in a remark-able 16x measurement speed,with the potential for further acceleration in the future.Our methodological advance enables highly efficient and accurate 3D surface reconstructions,thereby expanding the potential applications of confocal micros-copy in various scientific and industrial fields.

confocal microscopy3D surface imagingself-supervised learning

王泽昊、翁同天、陈向东、赵莉、孙方稳

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CAS Key Laboratory of Quantum Information,University of Science and Technology of China,Hefei 230026,China

CAS Center for Excellence in Quantum Information and Quantum Physics,University of Science and Technology of China,Hefei 230026,China

Hefei National Laboratory,University of Science and Technology of China,Hefei 230088,China

Anhui Golden-Shield 3D Technology Co.,Ltd.,Hefei 230011,China

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2024

10.3788/COL202422.060002

中国光学快报(英文版)
中国光学学会 中国科学院上海光学精密机械研究所

中国光学快报(英文版)

CSTPCD
影响因子：1.305
ISSN：1671-7694
年,卷(期)：2024.22(6)