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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.09953 |
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| _version_ | 1866909693074472960 |
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| author | Wang, Zifei Mao, Zian He, Xiaoya Huang, Xi Zhang, Haoran Cheng, Chun Chu, Shufen Hou, Tingzheng Zeng, Xiaoqin Xie, Yujun |
| author_facet | Wang, Zifei Mao, Zian He, Xiaoya Huang, Xi Zhang, Haoran Cheng, Chun Chu, Shufen Hou, Tingzheng Zeng, Xiaoqin Xie, Yujun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion Wang, Zifei Mao, Zian He, Xiaoya Huang, Xi Zhang, Haoran Cheng, Chun Chu, Shufen Hou, Tingzheng Zeng, Xiaoqin Xie, Yujun Computer Vision and Pattern Recognition 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. |
| title | 4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.09953 |