# Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning

Source: [Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning](<https://www.sota2.com/research/paper/697846a5a2b28e761d87c55d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-30T05:00:14.653Z

Authors: Matan Leibovich, Mai Tan, Ramon Manzorro, Adria Marcos-Morales, Sreyas Mohan, Peter A. Crozier, Carlos Fernandez-Granda

Published: 2026-01-20T19:35:53.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.17046>)

[PDF](<https://arxiv.org/pdf/2601.17046.pdf>)