4D-PreNet: A Unified Preprocessing Framework for 4D-STEM Data Analysis

TL;DR

4D-PreNet is a deep learning framework that enhances 4D-STEM data analysis by robustly correcting noise, beam drift, and distortions, enabling real-time, automated microscopy characterization.

Contribution

The paper introduces 4D-PreNet, a novel end-to-end deep learning pipeline that simultaneously performs denoising and distortion correction for 4D-STEM data, surpassing traditional methods.

Findings

Reduces mean squared error by up to 50% in denoising

Achieves sub-pixel accuracy in center localization

Outperforms traditional correction algorithms

Abstract

Automated experimentation with real time data analysis in scanning transmission electron microscopy (STEM) often require end-to-end framework. The four-dimensional scanning transmission electron microscopy (4D-STEM) with high-throughput data acquisition has been constrained by the critical bottleneck results from data preprocessing. Pervasive noise, beam center drift, and elliptical distortions during high-throughput acquisition inevitably corrupt diffraction patterns, systematically biasing quantitative measurements. Yet, conventional correction algorithms are often material-specific and fail to provide a robust, generalizable solution. In this work, we present 4D-PreNet, an end-to-end deep-learning pipeline that integrates attention-enhanced U-Net and ResNet architectures to simultaneously perform denoising, center correction, and elliptical distortion calibration. The network is trained on large, simulated datasets encompassing a wide range of noise levels, drift magnitudes, and distortion types, enabling it to generalize effectively to experimental data acquired under varying conditions. Quantitative evaluations demonstrate that our pipeline reduces mean squared error by up to 50% during denoising and achieves sub-pixel center localization in the center detection task, with average errors below 0.04 pixels. The outputs are bench-marked against traditional algorithms, highlighting improvements in both noise suppression and restoration of diffraction patterns, thereby facilitating high-throughput, reliable 4D-STEM real-time analysis for automated characterization.