用切片扩散先验实现大规模3D图像的高效重建
PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior
- 分片采样2D潜空间扩散模型，降低计算开销
- 在1024×1024×128大体积上提升重建质量
- 适合科学成像中需高保真重建的场景
扩散模型是贝叶斯逆问题中极具表现力的图像先验，但多数模型因训练和推理成本过高难以处理大规模高维数据。本文提出一种基于潜空间扩散先验的即插即用3D随机推断算法（PSI3D），用于处理大型体数据（1024×1024×128）。具体地，我们构建马尔可夫链蒙特卡洛方法，通过从2D潜空间扩散模型中采样来重建每一二维切片，并沿拼接轴引入随机总变差（TV）正则化以增强切片间一致性。我们在光学相干断层扫描（OCT）超分辨率任务上进行评估，结果表明，相比传统与学习基线方法，该方法显著提升了大规模科学成像的重建质量，同时提供鲁棒且可信的重建结果。
原文摘要 · Abstract (English)
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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