华南理工大学学报（自然科学版）2025，Vol.53Issue(11)：27-36,10.DOI:10.12141/j.issn.1000-565X.250032
基于材料结构条件先验的高噪声STEM图像原子结构分割方法
An Atomic Structure Segmentation Method for High-Noise STEM Image Based on Materials Structural Priors
摘要
Abstract
Scanning transmission electron microscopy(STEM)can perform electron imaging of material properties at the atomic picometer level and interpret the atomic structure using the obtained images.However,obtaining high-quality atomic-scale STEM images requires advanced STEM equipment and skilled operators.Various environmen-tal factors can introduce unpredictable non-uniform noise during the STEM imaging process,thereby significantly af-fecting image quality and consequently influencing the results of atomic structure analysis.The prediction model based on deep neural networks can reduce the impact of noise through denoising or data fitting,but there exists a problem of overfitting.This paper introduces materials structure conditions as priors in the deep neural network model and designs a method for atomic structure segmentation of high-noise STEM images based on materials struc-tural priors.In this method,the materials structural priors are modelled as the attention(including self-attention and cross-attention)of the segmentation network and are calculated,which not only enables the segmentation net-work to adaptively focus on the key regions of the image but also to adaptively focus on the control information from the structural coordinate vector modalities.In the simulation test set,as compared with AtomAI Segmentor method,the proposed method improves the chamfer distance,Jaccard and F1 metrics by 175%,49.7%and 42.7%,respec-tively;as compared with the early multi-scale method proposed by the research group,it improves the chamfer dis-tance,Jaccard and F1 metrics by 167%,28%and 23.9%,respectively.In the laboratory sample test set,as com-pared with AtomAI Segmentor method,the proposed method improves the chamfer distance,Jaccard and F1 metrics by 63%,9.3%and 7.4%,respectively;as compared with the early multi-scale method proposed by the research group,it improves the chamfer distance by 12.8%,and the Jaccard and F1 metrics remain largely unchanged.The introduction of materials structural priors enables the segmentation network model to more accurately segment the atomic structure in high-noise STEM images and predict the secondary structure information that is affected by noise or top-level occlusion.关键词
原子结构分割/扫描透射电子显微镜/高噪声图像/结构先验建模Key words
atomic structure segmentation/scanning transmission electron microscopy/high-noise image/structural prior modelling分类
信息技术与安全科学引用本文复制引用
郭礼华,林延域,陈轲..基于材料结构条件先验的高噪声STEM图像原子结构分割方法[J].华南理工大学学报（自然科学版）,2025,53(11):27-36,10.基金项目
广东省自然科学基金项目(2023A1515011014) Supported by the Natural Science Foundation of Guangdong Province(2023A1515011014) （2023A1515011014）
