BAM! Bayesian Anything Model: a foundation model for generative computational imaging
BAM introduces a lightweight foundation model for Bayesian computational imaging that performs few-step, physics-aware posterior sampling. It upgrades an existing network into a conditional flow map, allowing it to generalize robustly to new data and tasks with minimal tuning. This enables high-quality image reconstructions at a fraction of the cost.