Mixture-of-Gaussians-Guided Schedule Design for Brownian Bridge Diffusion Models
Brownian Bridge Diffusion Models (BBDM) offer an appealing framework for image restoration and inverse problems by constructing a stochastic bridge from the clean signal directly to the degraded observation, rather than to pure noise. Despite their promise, the choice of bridge schedule is typically inherited from heuristics, and a principled analytical framework for schedule design has been lacking. In this work, we develop such a framework by offering a novel analysis of BBDM reverse dynamics under a Mixture-of-Gaussians (MoG) prior. This setting yields a closed-form ideal posterior and a corresponding MMSE denoiser, while the BBDM-induced reconstruction law is captured analytically thr...
| Authors | Ron Levi, Michael Elad |
|---|---|
| Primary category | cs.LG |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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