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cs.CV2026
BAM! Bayesian Anything Model: a foundation model for generative computational imaging
Alessio Spagnoletti, Charlesquin Kemajou Mbakam, Jonathan Spence +2
Generative models are transforming Bayesian computational imaging, yet the field still lacks physics-aware foundation models. Current practice falls into two camps. Large foundatio…
cs.CV2025
Learning few-step posterior samplers by unfolding and distillation of diffusion models
Charlesquin Kemajou Mbakam, Jonathan Spence, Marcelo Pereyra
Diffusion models (DMs) have emerged as powerful image priors in Bayesian computational imaging. Two primary strategies have been proposed for leveraging DMs in this context: Plug-a…