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

TL;DR

This paper introduces a statistical characteristic-guided denoising network tailored for high-resolution transmission electron microscopy images, effectively reducing noise in rapid imaging scenarios to improve atomic observation.

Contribution

It proposes a novel denoising network guided by statistical characteristics in both spatial and frequency domains, specifically designed for HRTEM images, with a new noise calibration method and dataset.

Findings

Outperforms state-of-the-art denoising methods on synthetic and real HRTEM data.

Enhances localization accuracy in nucleation observation tasks.

Demonstrates robustness in noisy, rapid imaging conditions.

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.