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 s...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.展开更多
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)+1 种基金the National Natural Science Foundation of China (No. 62225506)the Anhui Provincial Key Research and Development Plan (No. 2022b13020006)。
文摘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.
