Improving axial resolution in Structured Illumination Microscopy using deep learning.
Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.
['Boland|Miguel A|MA|', 'Cohen|Edward A K|EAK|', 'Flaxman|Seth R|SR|', 'Neil|Mark A A|MAA|']
D000818:Animals D002843:Chromatin D003198:Computer Simulation D000077321:Deep Learning D006801:Humans D007091:Image Processing, Computer-Assisted D021621:Imaging, Three-Dimensional D018613:Microscopy, Confocal D008856:Microscopy, Fluorescence D055620:Optical Phenomena
Structured Illumination Microscopy structure illumination microscopy deep learning residual channel attention network