Deep learning approach for nonlinear structured illumination microscopy

Chang Ling; Luping Du; Xiaocong Yuan

doi:10.1117/12.2576839

5 November 2020 Deep learning approach for nonlinear structured illumination microscopy

Chang Ling, Luping Du, Xiaocong Yuan

Proceedings Volume 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics; 115650P (2020) https://doi.org/10.1117/12.2576839
Event: Applied Optics and Photonics China (AOPC 2020), 2020, Beijing, China

Abstract

We propose that Deep Learning (DL) can be used to improve the performance of nonlinear structured illumination microscopy(NSIM) to enable it to reconstruct a super-resolution image with much less raw image frames. This allows for gentler super-resolution imaging at higher speeds and weakens phototoxicity in the NSIM imaging process. We validate our approach by super-resolution image reconstruction of simulated obtained data.

Citation Download Citation

Chang Ling, Luping Du, and Xiaocong Yuan "Deep learning approach for nonlinear structured illumination microscopy", Proc. SPIE 11565, AOPC 2020: Display Technology; Photonic MEMS, THz MEMS, and Metamaterials; and AI in Optics and Photonics, 115650P (5 November 2020); https://doi.org/10.1117/12.2576839

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