Multi-Bridge Denoising Diffusion Probabilistic Models

Image-to-image translation aims to generate a target image conditioned on an observed source. While diffusion bridge models perform well in one-to-one settings, they are not adapted to problems where multiple sources provide complementary information about a single target. We introduce Multi-Bridge Denoising Diffusion Probabilistic Models (MB-DDPMs), a framework for many-to-one translation that builds stochastic bridges between the target and each source, coupled through a unified reverse process that aggregates information across sources. We evaluate MB-DDPM on brain MRI for Multiple Sclerosis to predict FLAIR contrast from T1, T2 and PD contrasts. We demonstrate strong clinical relevance through near-perfect agreement with ground-truth lesion-based measurements, highlighting its potential for reliable downstream medical analysis. We further evaluate MB-DDPM on image restoration using CIFAR-10 and ImageNet. Across all settings, MB-DDPM achieves state-of-the-art performance, consistently outperforming existing baselines in both reconstruction quality and perceptual metrics.