PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior

• URL: http://arxiv.org/abs/2512.18367v1
• Date: Sat, 20 Dec 2025 13:37:22 GMT
• Title: PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior
• Authors: Wenhan Guo, Jinglun Yu, Yaning Wang, Jin U. Kang, Yu Sun,
• Abstract summary: We introduce a Plugand-play algorithm for 3D inference with latent diffusion prior (PSI3D)<n>Specifically, we formulate a Markov chain Monte Carlo approach to reconstruct each two-dimensional (2D) slice by sampling from a 2D latent diffusion model.
• Score: 5.104613802755622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models are highly expressive image priors for Bayesian inverse problems. However, most diffusion models cannot operate on large-scale, high-dimensional data due to high training and inference costs. In this work, we introduce a Plug-and-play algorithm for 3D stochastic inference with latent diffusion prior (PSI3D) to address massive ($1024\times 1024\times 128$) volumes. Specifically, we formulate a Markov chain Monte Carlo approach to reconstruct each two-dimensional (2D) slice by sampling from a 2D latent diffusion model. To enhance inter-slice consistency, we also incorporate total variation (TV) regularization stochastically along the concatenation axis. We evaluate our performance on optical coherence tomography (OCT) super-resolution. Our method significantly improves reconstruction quality for large-scale scientific imaging compared to traditional and learning-based baselines, while providing robust and credible reconstructions.

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