{"ID":2832104,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06977","arxiv_id":"2512.06977","title":"Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging","abstract":"Accurate multi-slice reconstruction from limited measurement data is crucial to speed up the acquisition process in medical and scientific imaging. However, it remains challenging due to the ill-posed nature of the problem and the high computational and memory demands. We propose a framework that addresses these challenges by integrating partitioned diffusion priors with physics-based constraints. By doing so, we substantially reduce memory usage per GPU while preserving high reconstruction quality, outperforming both physics-only and full multi-slice reconstruction baselines for different modalities, namely Magnetic Resonance Imaging (MRI) and four-dimensional Scanning Transmission Electron Microscopy (4D-STEM). Additionally, we show that the proposed method improves in-distribution accuracy as well as strong generalization to out-of-distribution datasets.","short_abstract":"Accurate multi-slice reconstruction from limited measurement data is crucial to speed up the acquisition process in medical and scientific imaging. However, it remains challenging due to the ill-posed nature of the problem and the high computational and memory demands. We propose a framework that addresses these challe...","url_abs":"https://arxiv.org/abs/2512.06977","url_pdf":"https://arxiv.org/pdf/2512.06977v1","authors":"[\"Laurentius Valdy\",\"Richard D. Paul\",\"Alessio Quercia\",\"Zhuo Cao\",\"Xuan Zhao\",\"Hanno Scharr\",\"Arya Bangun\"]","published":"2025-12-07T20:07:12Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}