Fast structured illumination microscopy via deep learning
Chang Ling, Chonglei Zhang, Mingqun Wang, Fanfei Meng, Luping Du, Xiaocong Yuan
Abstract
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.