Particle localization and -classification constitute two of the most
fundamental problems in computational microscopy. In recent years, deep
learning based approaches have been introduced for these tasks with great
success. A key shortcoming of these supervised learning methods is their need
for large training data sets, typically generated from particle models in
conjunction with complex numerical forward models simulating the physics of
transmission electron microscopes. Computer implementations of such forward
models are computationally extremely demanding and limit the scope of their
applicability. In this paper we propose a method for simulating the forward
operator of an electron microscope based on additive noise and Neural Style
Transfer techniques. We evaluate the method on localization and classification
tasks using one of the established state-of-the-art architectures showing
performance on par with the benchmark. In contrast to previous approaches, our
method accelerates the data generation process by a factor of 750 while using
33 times less memory and scales well to typical transmission electron
microscope detector sizes. It utilizes GPU acceleration and parallel
processing. It can be used to adapt a synthetic training data set according to
reference data from any transmission electron microscope. The source code is
available at https://gitlab.com/deepet/faket.Comment: 18 pages, 1 table, 16 figures. Included fine-tuning, ablation, and
noiseless experiment