Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914185873457152 |
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| author | Valdy, Laurentius Paul, Richard D. Quercia, Alessio Cao, Zhuo Zhao, Xuan Scharr, Hanno Bangun, Arya |
| author_facet | Valdy, Laurentius Paul, Richard D. Quercia, Alessio Cao, Zhuo Zhao, Xuan Scharr, Hanno Bangun, Arya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging Valdy, Laurentius Paul, Richard D. Quercia, Alessio Cao, Zhuo Zhao, Xuan Scharr, Hanno Bangun, Arya Image and Video Processing Machine Learning 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. |
| title | Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2512.06977 |