Fast structured illumination microscopy via deep learning

• 作者: CHANG LING CHONGLEI ZHANG FANFEI MENG LUPING DU MINGQUN WANG XIAOCONG YUAN
• 来源期刊: 光子学研究:英文版
• 学科: 工学
• 关键词: NETWORKS ILLUMINATION FRAMES
• 年，卷（期）: 2020,（8）
• 页码范围: 1350-1359
• 页数: 10页
• 分类号: TP3

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.