Direct Diffusion Bridge using Data Consistency for Inverse Problems

Understand

Diffusion model-based inverse problem solvers have shown impressive performance, but are limited in speed, mostly as they require reverse diffusion sampling starting from noise.

• Several recent works have tried to alleviate this problem by building a diffusion process, directly bridging the clean and the corrupted for specific inverse problems.
• In this paper, we first unify these existing works under the name Direct Diffusion Bridges (DDB), showing that while motivated by different theories, the resulting algorithms only differ in the choice of parameters.
• Then, we highlight a critical limitation of the current DDB framework, namely that it does not ensure data consistency.