Deep-learning-based methods for super-resolution fluorescence microscopy

作     者：Jianhui Liao Junle Qu Yongqi Hao Jia Li 

作者机构：Shenzhen Key Laboratory of Photonics and Biophotonics Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060P.R.China NARI Group Corporation(State Grid Electric Power Research Institute)NARI Technology Co.Ltd.Nanjing 211106P.R.China 

出 版 物：《Journal of Innovative Optical Health Sciences》 (创新光学健康科学杂志（英文）)

年 卷 期：2023年第16卷第3期

页      面：85-100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)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key R&D Program of China(2021YFF0502900) the National Natural Science Foundation of China(61835009/62127819) 

主　　题：Super-resolution fuorescence microscopy deep learning convolutional neural net-work generative adversarial network image reconstruction 

摘      要：The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence ***-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly *** firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss ***,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.