Discovering Unseen Degradations to Adapt Open-World Image Restoration

While all-in-one image restoration models excel in controlled, closed-set environments, they face critical limitations when deployed in open-world settings where paired supervision is unavailable and degradations are unknown and often intricately mixed. To address this challenge, we propose a continual restoration framework that integrates degradation discovery with adaptive restoration. Building on a model pre-trained on known degradation categories, our method discovers unseen degradations in feature space and continually expands the degradation space. We then introduce a discovery- and instance-conditioned descriptor that jointly captures discovered degradation priors and image-specific characteristics, enabling robust restoration under ambiguous and intricately mixed degradations. To further support adaptation under unpaired supervision, we adopt a mean-teacher-based semi-supervised framework equipped with a reliable bank for pseudo-target refinement. Specifically, we propose a discovery-adaptive score that assesses pseudo-target reliability using feature-space distances to degradation categories, enabling pseudo-target selection to remain adaptive to newly discovered degradations rather than relying solely on conventional perceptual quality-based scoring methods. Experiments on both controlled open-world protocols and real-world adverse weather datasets demonstrate that our approach consistently outperforms existing methods across multiple image quality metrics while improving robustness to degradation composition shifts.