Deep learning-based single-shot structured illumination microscopy. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Qinnan; Chen, Jiawei; Li, Jiaosheng; Bo, En; Jiang, Heming; Lu, Xiaoxu; Zhong, Liyun; Tian, Jindong
• Is Part Of: Optics and lasers in engineering. Volume 155(2022)
• Publication Date: 2022-08
• Subjects: Structured illumination microscopy -- Super-resolution -- Deep learning; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.107066
• Languages: English
• ISSNs: 0143-8166

Abstract

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.