Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding

arXiv:2609.30539 · eess.SP, cs.LG, stat.ML · Submitted 2026-09-24 · Read on arXiv

eess.SP, cs.LG, stat.ML

Submitted: 2026-09-24

Updated: 2026-09-24

Comments: 5 pages, 2 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Inverse Problem — 9× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Aperture — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes

Sources

• A Survey on Diffusion Models for Inverse Problems
• Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging
• Diffusion-Prior Split Gibbs Sampling for Synthetic Aperture Radar Imaging under Incomplete Measurements
• Deep Unfolding: Recent Developments, Theory, and Design Guidelines

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