链式恢复：多任务图像恢复模型是零样本逐步通用图像修复器

尽管先前的图像修复(IR)方法往往聚焦于单一退化，但近期研究越来越多地关注解决涉及多种单一退化复合的复杂问题。然而，当前针对复合退化的IR方法需要构建包含指数级可能退化组合的训练数据，这带来巨大的负担。为缓解此问题，本文提出新任务设置，即通用图像修复(UIR)。具体而言，UIR无需在所有退化组合上训练，仅需在一组退化基上训练，然后以零样本方式移除这些基可能组合的任何退化。drawing inspiration from Chain-of-Thought that prompts large language models (LLMs) to address problems step-by-step, we propose Chain-of-Restoration (CoR) mechanism, which instructs models to remove unknown composite degradations step-by-step. By integrating a simple Degradation Discriminator into pre-trained multi-task models, CoR facilitates the process where models remove one degradation basis per step, continuing this process until the image is fully restored from the unknown composite degradation. Extensive experiments show that CoR can significantly improve model performance in removing composite degradations, achieving comparable or better results than those state-of-the-art (SoTA) methods trained on all degradations.