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

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.

@article{wang2025_2505.14916,
  title={ Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors },
  author={ Yaning Wang and Jinglun Yu and Wenhan Guo and Yu Sun and Jin U. Kang },
  journal={arXiv preprint arXiv:2505.14916},
  year={ 2025 }
}