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 simple 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 as a stand-alone method to adapt a training data set or as a data augmentation technique. The source code is available at https://gitlab.com/deepet/faket.

翻译：粒子定位与分类是计算显微学中最基本的两个问题。近年来，基于深度学习的方法被引入这些任务并取得了巨大成功。这些监督学习方法的一个关键缺陷在于需要大规模训练数据集——通常通过粒子模型结合模拟透射电子显微镜物理过程的复杂数值正向模型生成。此类正向模型的计算机实现计算成本极高，限制了其应用范围。本文提出一种基于加性噪声与神经风格迁移技术的简单方法，用于模拟电子显微镜的正向算子。我们采用一种成熟的先进架构，在定位与分类任务上对该方法进行了评估，结果显示其性能与基准相当。与先前方法相比，我们的方法将数据生成过程加速了750倍，同时内存消耗减少33倍，且能良好扩展至典型透射电子显微镜探测器尺寸。该方法支持GPU加速与并行处理，既可作为独立方法适配训练数据集，也可用作数据增强技术。源代码已开源至 https://gitlab.com/deepet/faket。