Unsupervised Deep Denoising for Four-Dimensional Scanning Transmission Electron Microscopy

Alireza Sadri,Timothy C. Petersen, Emmanuel W. C. Terzoudis-Lumsden,Bryan D. Esser,Joanne Etheridge,Scott D. Findlay

Microscopy and Microanalysis（2024）

Monash Univ

Cited 0|Views1

Abstract

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.

Translated text

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper