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

作　　者：Ze-Hao Wang Tong-Tian Weng Xiang-Dong Chen Li Zhao Fang-Wen Sun 王泽昊;翁同天;陈向东;赵莉;孙方稳(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)

机构地区：[1]CAS Key Laboratory of Quantum Information,University of Science and Technology of China,Hefei 230026,China [2]CAS Center for Excellence in Quantum Information and Quantum Physics,University of Science and Technology of China,Hefei 230026,China [3]Hefei National Laboratory,University of Science and Technology of China,Hefei 230088,China [4]Anhui Golden-Shield 3D Technology Co.,Ltd.,Hefei 230011,China

出　　处：《Chinese Optics Letters》2024年第6期3-7,共5页中国光学快报（英文版）

基　　金：supported by the Innovation Program for Quantum Science and Technology (No.2021ZD0303200);the CAS Project for Young Scientists in Basic Research (No.YSBR-049);the National Natural Science Foundation of China (No.62225506);the Anhui Provincial Key Research and Development Plan (No.2022b13020006)。

摘　　要：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 demonstrations 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 remarkable 16×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 microscopy in various scientific and industrial fields.

关 键 词：confocal microscopy 3D surface imaging self-supervised learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]