High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning

The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the limitation of the optical diffraction limit, it is still a challenging task to accurately distinguish two or more nanoparticles whose distance is less than the diffraction limit. Here, we propose a computational strategy based on a deep learning framework (NanoNet), which will realize the effective segmentation of the scattered light spots in diffraction-limited DFM images and obtain high-resolution plasmonic light scattering imaging. A small data set of DFM and the corresponding scanning electron microscopy (SEM) image pairs are used to learn for obtaining a highly resolved semantic imaging model using NanoNet, and thus highly resolved DFM images matching the resolution of those acquired using SEM can be obtained. Our method has the ability to transform diffraction-limited DFM images to highly resolved ones without adding a complex optical system. As a proof of concept, a highly resolved DFM image of living cells through the NanoNet technique is successfully made, opening up a new avenue for high-resolution optical nanoscopic imaging.