{"ID":2891441,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17800","arxiv_id":"2507.17800","title":"Improving Multislice Electron Ptychography with a Generative Prior","abstract":"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.","short_abstract":"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 du...","url_abs":"https://arxiv.org/abs/2507.17800","url_pdf":"https://arxiv.org/pdf/2507.17800v2","authors":"[\"Christian K. Belardi\",\"Chia-Hao Lee\",\"Yingheng Wang\",\"Justin Lovelace\",\"Kilian Q. Weinberger\",\"David A. Muller\",\"Carla P. Gomes\"]","published":"2025-07-23T16:35:25Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cond-mat.mtrl-sci\",\"cs.CV\",\"physics.optics\"]","methods":"[\"Diffusion Model\"]","has_code":false}