High-resolution electron microscopy (HREM) imaging technique is a powerful tool for directly visualizing a broad range of materials in real-space. However, it faces challenges in denoising due to ultra-low signal-to-noise ratio (SNR) and scarce data availability. In this work, we propose Noise2SR, a zero-shot self-supervised learning (ZS-SSL) denoising framework for HREM. Within our framework, we propose a super-resolution (SR) based self-supervised training strategy, incorporating the Random Sub-sampler module. The Random Sub-sampler is designed to generate approximate infinite noisy pairs from a single noisy image, serving as an effective data augmentation in zero-shot denoising. Noise2SR trains the network with paired noisy images of different resolutions, which is conducted via SR strategy. The SR-based training facilitates the network adopting more pixels for supervision, and the random sub-sampling helps compel the network to learn continuous signals enhancing the robustness. Meanwhile, we mitigate the uncertainty caused by random-sampling by adopting minimum mean squared error (MMSE) estimation for the denoised results. With the distinctive integration of training strategy and proposed designs, Noise2SR can achieve superior denoising performance using a single noisy HREM image. We evaluate the performance of Noise2SR in both simulated and real HREM denoising tasks. It outperforms state-of-the-art ZS-SSL methods and achieves comparable denoising performance with supervised methods. The success of Noise2SR suggests its potential for improving the SNR of images in material imaging domains.

翻译：高分辨率电子显微成像技术是一种在实空间直接观测多种材料的强大工具。然而，由于极低的信噪比和稀缺的数据可用性，其在去噪方面面临挑战。本文提出Noise2SR，一种用于高分辨率电子显微成像的零样本自监督学习去噪框架。在该框架中，我们提出一种基于超分辨率的自监督训练策略，并引入了随机子采样器模块。随机子采样器旨在从单张噪声图像生成近似无限的噪声对，作为零样本去噪中有效的数据增强手段。Noise2SR通过超分辨率策略，使用不同分辨率的配对噪声图像训练网络。基于超分辨率的训练促使网络采用更多像素进行监督，而随机子采样有助于迫使网络学习连续信号，从而增强鲁棒性。同时，我们通过对去噪结果采用最小均方误差估计来减轻随机采样带来的不确定性。凭借训练策略与所提设计的独特整合，Noise2SR能够仅使用单张噪声高分辨率电子显微图像实现卓越的去噪性能。我们在模拟和真实高分辨率电子显微去噪任务中评估了Noise2SR的性能。其表现优于最先进的零样本自监督学习方法，并与监督方法取得了相当的去噪性能。Noise2SR的成功表明其在提升材料成像领域图像信噪比方面具有潜力。