A generalized deep neural network approach for improving resolution of fluorescence microscopy images  

作　　者：Zichen Jin Qing He Yang Liu Kaige Wang 

机构地区：[1]State Key Laboratory of Cultivation Base for Photoelectric Technology and Functional Materials,National Center for International Research of Photoelectric Technology&Nano-Functional Materials and Application,Key Laboratory of Photoelectronic Technology of Shaanxi Province,Institute of Photonics and Photon-Technology,Northwest University,Xi'an 710127,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2024年第6期53-65,共13页创新光学健康科学杂志（英文）

基　　金：Subjects funded by the National Natural Science Foundation of China(Nos.62275216 and 61775181);the Natural Science Basic Research Programme of Shaanxi Province-Major Basic Research Special Project(Nos.S2018-ZC-TD-0061 and TZ0393);the Special Project for the Development of National Key Scientific Instruments and Equipment No.(51927804).

摘　　要：Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

关 键 词：Deep learning super-resolution imaging generalized model framework generation adversarial networks image reconstruction. 

分 类 号：TH742[机械工程—光学工程]