UP-Restorer: When Unrolling Meets Prompts for Unified Image Restoration

All-in-one restoration needs to implicitly distinguish between different degradation conditions and apply specific prior constraints accordingly. To fulfill this goal, our work makes the first effort to create an all-in-one restoration via unrolling from the typical maximum a-posterior optimization function. This unrolling framework naturally leads to the construction of progressively solving models, which are equivalent to a diffusion enhancer taking as input dynamically generated prompts. Under a score-based diffusion model, the prompts are integrated for propagating and updating several context-related variables, i.e. transmission map, atmospheric light map, and noise or rain map progressively. Such a learned prompt generation process, which simulates the nonlinear operations in the unrolled solution, is combined with linear operations owning clear physics implications to make the diffusion models well-regularized and more effective in learning degradation-related visual priors. Experimental results demonstrate that our method achieves significant performance improvements across various image restoration tasks, realizing true all-in-one image restoration.