  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.
