Statistical Characteristic-Guided Denoising for Rapid High-Resolution Transmission Electron Microscopy Imaging

2026-03-19

AI Summary

This work addresses the challenge of accurately identifying atomic positions in short-exposure, high-speed, high-resolution transmission electron microscopy (HRTEM) images, which are severely degraded by strong noise. To this end, the authors propose a guided denoising network that jointly leverages spatial-domain bias and frequency-band statistical features. The method integrates a spatial-bias-guided weighted convolution module with a frequency-band-guided weighted filtering mechanism, underpinned by HRTEM-specific noise modeling, a realistically synthesized noise dataset, and a dedicated denoising architecture. This approach represents the first effort to enable efficient denoising driven by combined statistics from both spatial and frequency domains. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art techniques on both synthetic and real HRTEM data, significantly improving the accuracy of downstream tasks such as atomic localization.

Technology Category

• Machine Learning: Kernel Methods
• Computer Vision: Other Foundations of Computer Vision
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

High-Resolution Transmission Electron Microscopy (HRTEM) enables atomic-scale observation of nucleation dynamics, which boosts the studies of advanced solid materials. Nonetheless, due to the millisecond-scale rapid change of nucleation, it requires short-exposure rapid imaging, leading to severe noise that obscures atomic positions. In this work, we propose a statistical characteristic-guided denoising network, which utilizes statistical characteristics to guide the denoising process in both spatial and frequency domains. In the spatial domain, we present spatial deviation-guided weighting to select appropriate convolution operations for each spatial position based on deviation characteristic. In the frequency domain, we present frequency band-guided weighting to enhance signals and suppress noise based on band characteristics. We also develop an HRTEM-specific noise calibration method and generate a dataset with disordered structures and realistic HRTEM image noises. It can ensure the denoising performance of models on real images for nucleation observation. Experiments on synthetic and real data show our method outperforms the state-of-the-art methods in HRTEM image denoising, with effectiveness in the localization downstream task. Code will be available at https://github.com/HeasonLee/SCGN.

Problem

• HRTEM
• denoising
• nucleation dynamics
• noise
• atomic-scale imaging

Innovation

• statistical characteristic-guided denoising
• spatial deviation-guided weighting
• frequency band-guided weighting
• HRTEM noise calibration
• atomic-scale imaging