The vast majority of transmission electron microscopy (TEM) data never gets published and ends up on a backup drive until deleted to free up space. These left-over datasets are rich in detail and variation, often paired with automatically saved metadata of instrument state and acquisition parameters. In this work, we introduce a dataset of 7,330 high-angle annular dark-field scanning-TEM (HAADF-STEM) images from a single instrument to learn a joint embedding space between image metadata and HAADF image. These embeddings link image style with acquisition parameters, which allows us to train a generative style transfer network that can convert experimental images into the style they would have had if they were recorded with different instrument parameters. We evaluate the performance of the network and explore the usefulness of the technique for physical denoising.

翻译：绝大多数透射电子显微镜（TEM）数据从未被发表，最终被存储在备份硬盘中，直至因释放空间而被删除。这些遗留数据集富含细节和多样性，通常与自动保存的仪器状态和采集参数元数据相关联。本研究引入了一个包含7,330幅高角环形暗场扫描透射电子显微镜（HAADF-STEM）图像的数据集，该数据集来自同一台仪器，用于学习图像元数据与HAADF图像之间的联合嵌入空间。这些嵌入将图像风格与采集参数联系起来，从而能够训练一个生成式风格迁移网络，将实验图像转换为以不同仪器参数记录时所应具有的风格。我们评估了该网络的性能，并探讨了该技术在物理去噪方面的实用性。