Ptychographic Image Reconstruction from Limited Data via Score-Based Diffusion Models with Physics-Guidance
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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_ | 1866918017991966720 |
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| author | Cam, Refik Mert Deng, Junjing Kettimuthu, Rajkumar Cherukara, Mathew J. Bicer, Tekin |
| author_facet | Cam, Refik Mert Deng, Junjing Kettimuthu, Rajkumar Cherukara, Mathew J. Bicer, Tekin |
| contents | Ptychography is a data-intensive computational imaging technique that achieves high spatial resolution over large fields of view. The technique involves scanning a coherent beam across overlapping regions and recording diffraction patterns. Conventional reconstruction algorithms require substantial overlap, increasing data volume and experimental time, reaching PiB-scale experimental data and weeks to month-long data acquisition times. To address this, we propose a reconstruction method employing a physics-guided score-based diffusion model. Our approach trains a diffusion model on representative object images to learn an object distribution prior. During reconstruction, we modify the reverse diffusion process to enforce data consistency, guiding reverse diffusion toward a physically plausible solution. This method requires a single pretraining phase, allowing it to generalize across varying scan overlap ratios and positions. Our results demonstrate that the proposed method achieves high-fidelity reconstructions with only a 20% overlap, while the widely employed rPIE method requires a 62% overlap to achieve similar accuracy. This represents a significant reduction in data requirements, offering an alternative to conventional techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ptychographic Image Reconstruction from Limited Data via Score-Based Diffusion Models with Physics-Guidance Cam, Refik Mert Deng, Junjing Kettimuthu, Rajkumar Cherukara, Mathew J. Bicer, Tekin Image and Video Processing Ptychography is a data-intensive computational imaging technique that achieves high spatial resolution over large fields of view. The technique involves scanning a coherent beam across overlapping regions and recording diffraction patterns. Conventional reconstruction algorithms require substantial overlap, increasing data volume and experimental time, reaching PiB-scale experimental data and weeks to month-long data acquisition times. To address this, we propose a reconstruction method employing a physics-guided score-based diffusion model. Our approach trains a diffusion model on representative object images to learn an object distribution prior. During reconstruction, we modify the reverse diffusion process to enforce data consistency, guiding reverse diffusion toward a physically plausible solution. This method requires a single pretraining phase, allowing it to generalize across varying scan overlap ratios and positions. Our results demonstrate that the proposed method achieves high-fidelity reconstructions with only a 20% overlap, while the widely employed rPIE method requires a 62% overlap to achieve similar accuracy. This represents a significant reduction in data requirements, offering an alternative to conventional techniques. |
| title | Ptychographic Image Reconstruction from Limited Data via Score-Based Diffusion Models with Physics-Guidance |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2502.18767 |