Fast structured illumination microscopy via deep learning

作     者：CHANG LING CHONGLEI ZHANG MINGQUN WANG FANFEI MENG LUPING DU XIAOCONG YUAN 

作者机构：Nanophotonics Research CenterShenzhen Key Laboratory of Micro-Scale Optical Information Technology&Institute of Microscale OptoelectronicsShenzhen UniversityShenzhen 518060China 

出 版 物：《Photonics Research》 (光子学研究（英文版）)

年 卷 期：2020年第8卷第8期

页      面：1350-1359页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Science and Technology Innovation Commission of Shenzhen(KQTD2015071016560101,KQTD2017033011044403,ZDSYS201703031605029) Natural Science Foundation of Guangdong Province(2016A030312010) Leading Talents Program of Guangdong Province(00201505) National Natural Science Foundation of China(61490712,61622504,61775085,91850202) China Postdoctoral Science Foundation(2019M663048) 

主　　题：networks illumination frames 

摘      要：This study shows that convolutional neural networks(CNNs)can be used to improve the performance of structured illumination microscopy to enable it to reconstruct a super-resolution image using three instead of nine raw frames,which is the standard number of frames required to this *** to the isotropy of the fluorescence group,the correlation between the high-frequency information in each direction of the spectrum is obtained by training the CNNs.A high-precision super-resolution image can thus be reconstructed using accurate data from three image frames in one *** allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the imaging process.