We present a novel approach for extracting 3D atomic-level information from transmission electron microscopy (TEM) images affected by significant noise. The approach is based on formulating depth estimation as a semantic segmentation problem. We address the resulting segmentation problem by training a deep convolutional neural network to generate pixel-wise depth segmentation maps using simulated data corrupted by synthetic noise. The proposed method was applied to estimate the depth of atomic columns in CeO2 nanoparticles from simulated images and real-world TEM data. Our experiments show that the resulting depth estimates are accurate, calibrated and robust to noise.

翻译：我们提出了一种新颖方法，用于从受显著噪声影响的透射电子显微镜图像中提取三维原子级信息。该方法将深度估计问题构建为语义分割任务。我们通过训练深度卷积神经网络来解决由此产生的分割问题，该网络使用受合成噪声污染的模拟数据生成像素级深度分割图。所提方法被应用于从模拟图像和真实透射电子显微镜数据中估算CeO2纳米颗粒原子柱的深度。实验结果表明，所得深度估计结果准确、校准良好且对噪声具有鲁棒性。