Artificial Scanning Electron Microscopy Images Created by Generative Adversarial Networks from Simulated Particle Assemblies
Jonas Bals, Matthias Epple
Abstract
Particle assemblies created by software package Blender are converted into artificial scanning electron micrographs (SEM) with a generative adversarial network (GAN). The introduction of height maps (i.e., surface topography or relief structure) considerably enhances the quality of the artificial SEM images by providing 3D information on the input data. These artificial images serve as input data to train a convolutional neural network (CNN) to identify and classify nanoparticles. Although the performance of the CNN trained with artificial SEM images is slightly inferior to the same CNN trained with real SEM images, this offers a pathway to create training data for segmentation and classification networks for SEM image analysis by deep learning algorithms.