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.

翻译：摘要：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已成功应用于这些任务。这类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，这些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前向模型生成。此类前向模型的计算机实现计算量极大，严重限制了其适用范围。本文提出一种基于加性噪声和神经风格迁移技术的电子显微镜前向算子模拟方法。我们利用现有主流先进架构在定位与分类任务上对该方法进行评估，结果表明其性能与基准方法相当。与先前方法相比，本方法将数据生成速度提升750倍，同时内存消耗减少33倍，并能良好适配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，可依据任意透射电子显微镜的参考数据对合成训练数据集进行自适应调整。源代码已开源发布于https://gitlab.com/deepet/faket。