Universal image restoration via task-adaptive diffusion degradation oriented model

Image restoration covers many sub-tasks, including image super-resolution, inpainting, deblurring, compressed sensing, etc. However, existing methods often struggle to balance generality across tasks and specificity to degradation patterns. Multi-task methods relying on generative priors and neglect the diversity of degradation operators, leading to worse performance and hallucinations, while task-specific methods cannot capture the generality of different image restoration tasks. In this work, we introduce the Task-Adaptive Diffusion Degradation Oriented Model (DDOM), which bridges this gap by integrating a pre-trained diffusion model as a general generative prior with lightweight Degradation Oriented Adapters (DO-Adapters) to align task-specific knowledge. DO-Adapters extract task-specific priors and refine the diffusion process at each timestep, guiding the diffusion model toward accurate restoration while reducing hallucinations. This design decouples task adaptation from the pre-trained model, enabling plug-and-play deployment across tasks with low computation (0.36% of the pre-trained models). Experimental results demonstrate DDOM outperforms the superior multi-task methods, while matching or surpassing task-specific methods in visual quality. Notably, DDOM exhibits strong generalization in out-of-distribution datasets and extreme degradation scenarios, validating its effectiveness in unifying generality.