.png){kind=icon}
返回
Machine learning assisted quantum super-resolution microscopy
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-023-40506-4.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of vn improvement in the spatial resolution by measuring the n-th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.
Keyword:
RESOLUTION LIMIT
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
机构
引用论文
Model estimates of CO2 emissions from soil in response to global warming响应全球变暖的土壤CO2排放模型估计.png)
Nature
NatureIF0
PREAI
分享收藏Quantum correlation enhanced super-resolution localization microscopy enabled by a fibre bundle cameraNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)通过随机光学重建显微镜 (STORM) 进行亚衍射极限成像NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容