Using untrained, physics-informed neural networks for structured illumination image reconstruction (Conference Presentation)

Abstract

In recent years, new forms of structured illumination microscopy (SIM) have used near-field illumination from metamaterial substrates to increase resolution improvements past 2x. We demonstrate that the forward model of SIM can be used as the loss function to optimize a neural network on a single set of diffraction-limited sub-images. We show that this physics-informed neural network (PINN) can be used with a variety of structured illumination methods such as plasmonic and metamaterial SIM to achieve resolution improvements of 3x and 4x.