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 identifies unseen degradations by measuring deviations in feature space via Mahalanobis distance and performs continual category discovery to expand the degradation space. Based on this, we introduce a discovery- and instance-conditioned descriptor that combines degradation-aware representations derived from cluster prototypes with instance-specific features, enabling adaptive conditioning for mixed and ambiguous degradations. To further handle unpaired data, we adopt a mean-teacher-based semi-supervised framework with a reliable bank for pseudo-target refinement. Crucially, we propose a discovery-adaptive score that evaluates candidate targets based on their relative proximity to degradation clusters and an anchored clear cluster, enabling degradation-aware pseudo-target selection beyond conventional no-reference image quality assessment metrics. 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.