Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors

• URL: http://arxiv.org/abs/2505.14916v1
• Date: Tue, 20 May 2025 21:09:26 GMT
• Title: Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors
• Authors: Yaning Wang, Jinglun Yu, Wenhan Guo, Yu Sun, Jin U. Kang,
• Abstract summary: We propose an OCT super-resolution framework based on a plug-and-play diffusion model (DM-DM) to reconstruct high-quality images from corneal measurements.<n>Our method formulates as an inverse problem, combining a prior with sparse chain Monte Carlo sampling for efficient reconstruction.
• Score: 6.457037057474951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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