Deep learning-based single-shot structured illumination microscopy. (August 2022) - Zhang, Qinnan; Chen, Jiawei; Li, Jiaosheng; Bo, En; Jiang, Heming; Lu, Xiaoxu; Zhong, Liyun; Tian, Jindong
Highlights: A deep learning-based structured illumination microscopy method is proposed. Generative adversative networks and deformation of U-Net are used for this task. It can reconstruct a super-resolution image using only one frame image. It can greatly reduce phototoxicity and photobleaching. Abstract: We report a deep learning-based structured illumination microscopy (SIM) method, which can reconstruct a super-resolution (SR) image using only one frame structured illumination image. Generative adversative networks (GANs) and deformation of U-Net (DU-Net) are employed to perform the task. GANs are trained to generate other structured illumination images by feeding a single structured illumination image, and DU-Net is trained to reconstruct the super-resolution image. The results of experiments and simulations demonstrate that the SR image could be reconstructed from one frame structured illumination image. Importantly, it can greatly reduce phototoxicity and photobleaching.