Universal image restoration is a practical and potential computer vision task
for real-world applications. The main challenge of this task is handling the
different degradation distributions at once. Existing methods mainly utilize
task-specific conditions (e.g., prompt) to guide the model to learn different
distributions separately, named multi-partite mapping. However, it is not
suitable for universal model learning as it ignores the shared information
between different tasks. In this work, we propose an advanced selective
hourglass mapping strategy based on diffusion model, termed DiffUIR. Two novel
considerations make our DiffUIR non-trivial. Firstly, we equip the model with
strong condition guidance to obtain accurate generation direction of diffusion
model (selective). More importantly, DiffUIR integrates a flexible shared
distribution term (SDT) into the diffusion algorithm elegantly and naturally,
which gradually maps different distributions into a shared one. In the reverse
process, combined with SDT and strong condition guidance, DiffUIR iteratively
guides the shared distribution to the task-specific distribution with high
image quality (hourglass). Without bells and whistles, by only modifying the
mapping strategy, we achieve state-of-the-art performance on five image
restoration tasks, 22 benchmarks in the universal setting and zero-shot
generalization setting. Surprisingly, by only using a lightweight model (only
0.89M), we could achieve outstanding performance. The source code and
pre-trained models are available at this https URL

提出了一种基于扩散模型的高级选择性沙漏映射策略 DiffUIR，通过强约束引导模型获得精确的扩散模型生成方向，将不同分布逐渐映射到一个共享分布中，结合共享分布项和强约束引导，DiffUIR 迭代地将共享分布引导到具有高图像质量的特定任务分布，只通过修改映射策略，在五个图像恢复任务中，在通用设置和零样本泛化设置中取得了最先进的性能。