While electron microscopy offers crucial atomic-resolution insights into structure-property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of conventional electron microscopy by adapting principles from multi-image super-resolution (MISR) that have been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances the reconstruction with a convolutional neural network (CNN) that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for 4D-STEM that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate comparable spatial resolution to conventional ptychography under ultra-low-dose conditions. Our work expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of radiation-vulnerable materials.
@article{arxiv.2507.09953,
title = {4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion},
author = {Zifei Wang and Zian Mao and Xiaoya He and Xi Huang and Haoran Zhang and Chun Cheng and Shufen Chu and Tingzheng Hou and Xiaoqin Zeng and Yujun Xie},
journal= {arXiv preprint arXiv:2507.09953},
year = {2025}
}