arXiv Machine Learning
Aug 19

Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors

The paper introduces PiX-MC, a time‑parallel posterior sampling framework that combines proximal Langevin dynamics with Picard iteration for Bayesian imaging inverse problems. By leveraging efficient proximal operators for many imaging likelihoods and exploiting parallelism across discretization nodes, PiX-MC supports multi‑GPU implementation and includes multi‑block and annealed variants to enhance scalability. Experiments on various imaging tasks, including a large‑scale sparse‑view CT problem, show that PiX‑MC can reduce runtime by up to 50× while maintaining reconstruction quality.

By Deliang Wei, Evan Bell, Wenhan Guo, Yifan Chen, Yu Sun
arXiv Machine Learning
Aug 3

P-Flow: Proxy-gradient Flows for Linear Inverse Problems

arXiv:2605. 08328v3 Announce Type: replace Abstract: Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster sampling compared to diffusion models.

By Zehua Jiang, Fenghao Zhu, Xinquan Wang, Chongwen Huang, Zhaoyang Zhang