IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
This work addresses a domain-specific problem for image restoration researchers by providing a more flexible and adaptable approach, though it is incremental as it builds on existing bridge models.
The paper tackles the problem of high computational costs and limited performance in image restoration bridge models by efficiently leveraging pre-trained generative priors, resulting in improved robustness and generalization across six tasks.
Bridge models in image restoration construct a diffusion process from degraded to clear images. However, existing methods typically require training a bridge model from scratch for each specific type of degradation, resulting in high computational costs and limited performance. This work aims to efficiently leverage pretrained generative priors within existing image restoration bridges to eliminate this requirement. The main challenge is that standard generative models are typically designed for a diffusion process that starts from pure noise, while restoration tasks begin with a low-quality image, resulting in a mismatch in the state distributions between the two processes. To address this challenge, we propose a transition equation that bridges two diffusion processes with the same endpoint distribution. Based on this, we introduce the IRBridge framework, which enables the direct utilization of generative models within image restoration bridges, offering a more flexible and adaptable approach to image restoration. Extensive experiments on six image restoration tasks demonstrate that IRBridge efficiently integrates generative priors, resulting in improved robustness and generalization performance. Code will be available at GitHub.