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Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors — arXiv:2608.17666

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.17666
License (as archived): CC0-1.0
Archived: 2026-10-18T14:48:40.185Z
SHA-256 of the archived text: 80dcdba27a1bbec46dd4176623c9268fe03b004be7bcd52c340212575a18da48
Stable URL of this page: https://metraipolis.com/documento/dc_321bfcc49e1b

How to cite

Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors — arXiv:2608.17666 — arXiv (en), https://arxiv.org/abs/2608.17666 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_321bfcc49e1b (Metraipolis archive dc_321bfcc49e1b, sha256 80dcdba27a1bbec46dd4176623c9268fe03b004be7bcd52c340212575a18da48)

BibTeX

@misc{dc321bfcc49e1b,
  title = {Picard Proximal Monte Carlo for Parallel Bayesian Imaging with Score-Based Generative Priors — arXiv:2608.17666},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.17666},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_321bfcc49e1b; sha256 80dcdba27a1bbec46dd4176623c9268fe03b004be7bcd52c340212575a18da48},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.17666. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Bayesian imaging inverse problems often require sampling from high-dimensional posterior distributions. While recent score-based and diffusion models provide expressive Bayesian priors, their sampling procedures remain inherently sequential and computationally expensive for large-scale imaging applications. We propose PiX-MC, a time-parallel posterior sampling framework based on proximal Langevin dynamics and Picard iteration. The proximal-likelihood formulation exploits the fact that many imaging likelihoods admit efficient, problem-specific proximal operators, while Picard refinement exposes parallelism across discretization nodes and naturally supports multi-GPU implementation. To further improve practical scalability and sampling performance, we develop multi-block and annealed variants of the proposed framework. We establish convergence guarantees under transparent assumptions, accommodating non-log-concave posteriors, imperfect learned score models, multi-block implementations, and annealing schedules. Experiments on a diverse collection of imaging inverse problems demonstrate that PiX-MC substantially reduces wall-clock time while preserving reconstruction quality. On a $512\times512\times80$ sparse-view computed tomography (CT) problem, annealed multi-block PiX-MC achieves up to a $50\times$ runtime speedup over the standard Langevin sampler using eight GPUs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-19 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.17666 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T14:47:59.404Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-19?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).