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.

翻译：高分辨率透射电子显微镜（HRTEM）能够实现成核动态的原子尺度观测，从而推动先进固体材料的研究。然而，由于成核过程在毫秒量级内快速变化，需要短曝光快速成像，导致严重噪声掩盖原子位置。本文提出一种统计分析引导的去噪网络，利用统计特征在空间域和频率域中引导去噪过程。在空间域中，我们提出基于空间偏差引导的加权方法，根据偏差特征为每个空间位置选择适当的卷积操作。在频率域中，我们提出基于频带引导的加权方法，根据频带特征增强信号并抑制噪声。我们还开发了针对HRTEM的噪声标定方法，并生成了包含无序结构及真实HRTEM图像噪声的数据集。该方法可确保模型在成核观测真实图像上的去噪性能。在合成数据和真实数据上的实验表明，我们的方法在HRTEM图像去噪中优于现有方法，并在定位下游任务中展现出有效性。代码将开源在 https://github.com/HeasonLee/SCGN。