Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors
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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_ | 1866915295657984000 |
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| author | Wang, Yaning Yu, Jinglun Guo, Wenhan Sun, Yu Kang, Jin U. |
| author_facet | Wang, Yaning Yu, Jinglun Guo, Wenhan Sun, Yu Kang, Jin U. |
| contents | We propose an OCT super-resolution framework based on a plug-and-play diffusion model (PnP-DM) to reconstruct high-quality images from sparse measurements (OCT B-mode corneal images). Our method formulates reconstruction as an inverse problem, combining a diffusion prior with Markov chain Monte Carlo sampling for efficient posterior inference. We collect high-speed under-sampled B-mode corneal images and apply a deep learning-based up-sampling pipeline to build realistic training pairs. Evaluations on in vivo and ex vivo fish-eye corneal models show that PnP-DM outperforms conventional 2D-UNet baselines, producing sharper structures and better noise suppression. This approach advances high-fidelity OCT imaging in high-speed acquisition for clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors Wang, Yaning Yu, Jinglun Guo, Wenhan Sun, Yu Kang, Jin U. Image and Video Processing Computer Vision and Pattern Recognition We propose an OCT super-resolution framework based on a plug-and-play diffusion model (PnP-DM) to reconstruct high-quality images from sparse measurements (OCT B-mode corneal images). Our method formulates reconstruction as an inverse problem, combining a diffusion prior with Markov chain Monte Carlo sampling for efficient posterior inference. We collect high-speed under-sampled B-mode corneal images and apply a deep learning-based up-sampling pipeline to build realistic training pairs. Evaluations on in vivo and ex vivo fish-eye corneal models show that PnP-DM outperforms conventional 2D-UNet baselines, producing sharper structures and better noise suppression. This approach advances high-fidelity OCT imaging in high-speed acquisition for clinical applications. |
| title | Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.14916 |