Improving Multislice Electron Ptychography with a Generative Prior
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913959576076288 |
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| author | Belardi, Christian K. Lee, Chia-Hao Wang, Yingheng Lovelace, Justin Weinberger, Kilian Q. Muller, David A. Gomes, Carla P. |
| author_facet | Belardi, Christian K. Lee, Chia-Hao Wang, Yingheng Lovelace, Justin Weinberger, Kilian Q. Muller, David A. Gomes, Carla P. |
| contents | Multislice electron ptychography (MEP) is an inverse imaging technique that computationally reconstructs the highest-resolution images of atomic crystal structures from diffraction patterns. Available algorithms often solve this inverse problem iteratively but are both time consuming and produce suboptimal solutions due to their ill-posed nature. We develop MEP-Diffusion, a diffusion model trained on a large database of crystal structures specifically for MEP to augment existing iterative solvers. MEP-Diffusion is easily integrated as a generative prior into existing reconstruction methods via Diffusion Posterior Sampling (DPS). We find that this hybrid approach greatly enhances the quality of the reconstructed 3D volumes, achieving a 90.50% improvement in SSIM over existing methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_17800 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Multislice Electron Ptychography with a Generative Prior Belardi, Christian K. Lee, Chia-Hao Wang, Yingheng Lovelace, Justin Weinberger, Kilian Q. Muller, David A. Gomes, Carla P. Image and Video Processing Materials Science Computer Vision and Pattern Recognition Optics Multislice electron ptychography (MEP) is an inverse imaging technique that computationally reconstructs the highest-resolution images of atomic crystal structures from diffraction patterns. Available algorithms often solve this inverse problem iteratively but are both time consuming and produce suboptimal solutions due to their ill-posed nature. We develop MEP-Diffusion, a diffusion model trained on a large database of crystal structures specifically for MEP to augment existing iterative solvers. MEP-Diffusion is easily integrated as a generative prior into existing reconstruction methods via Diffusion Posterior Sampling (DPS). We find that this hybrid approach greatly enhances the quality of the reconstructed 3D volumes, achieving a 90.50% improvement in SSIM over existing methods. |
| title | Improving Multislice Electron Ptychography with a Generative Prior |
| topic | Image and Video Processing Materials Science Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2507.17800 |