Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning

TL;DR

This paper introduces a deep learning method that accurately estimates atomic depths from noisy electron microscopy images by treating the problem as semantic segmentation, demonstrating robustness on simulated and real data.

Contribution

The paper presents a novel deep learning approach that formulates atomic depth estimation as a segmentation task, improving accuracy and noise robustness in TEM data analysis.

Findings

Accurate depth estimation from noisy TEM images.

Robustness of the method to high noise levels.

Effective application to real-world TEM data.

Abstract

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.