用深度学习从噪声电子显微图像中精准估算原子层深度
Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning
- 将深度估计转化为像素级语义分割问题
- 在带噪模拟数据上训练网络，实现高精度深度预测
- 适用于纳米颗粒原子结构分析，尤其适合低信噪比数据
我们提出一种新方法，从受严重噪声影响的透射电子显微镜（TEM）图像中提取三维原子级信息。该方法将深度估计建模为语义分割问题，通过在加有合成噪声的模拟数据上训练深度卷积神经网络，生成像素级深度分割图。该方法应用于模拟图像和真实世界中的CeO2纳米颗粒TEM数据，成功估算出原子列的深度。实验表明，所得深度估计结果准确、校准良好且对噪声具有鲁棒性。
原文摘要 · Abstract (English)
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.
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