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 end.Owing 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 direction.This allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the imaging process.
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).
