Abstract

Recent developments using deep learning (DL) super-resolution in structured-illumination microscopy (SIM) have improved speed in two-dimensional (2D) image restoration and minimized the impact of noise. We explore extending this 2D DL technique to 3D by augmenting the 2D-convolutional layers to 3D in a U-Net DL network. We demonstrate experimentally that this extension improves lateral and axial resolution in the final 3D restoration compared to the resolution achieved by axially stacking the outputs of the 2D U-Net.

© 2023 The Author(s)