{"ID":2826762,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18367","arxiv_id":"2512.18367","title":"PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior","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.","short_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 (P...","url_abs":"https://arxiv.org/abs/2512.18367","url_pdf":"https://arxiv.org/pdf/2512.18367v1","authors":"[\"Wenhan Guo\",\"Jinglun Yu\",\"Yaning Wang\",\"Jin U. Kang\",\"Yu Sun\"]","published":"2025-12-20T13:37:22Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}