USIR: Unified Single-Step Image Restoration via Reward-Guided Privileged Self-Distillation

Image restoration is challenging due to diverse and coupled degradations. Existing all-in-one methods often rely on explicit degradation understanding or task-specific guidance, and their performance declines under complex degradations, while recent agentic systems further introduce iterative assessment, planning, and tool execution, incurring substantial computational overhead and potential error accumulation across stages. To this end, we propose USIR, a Unified Single-step Image Restoration framework that directly restores degraded inputs in a single forward pass. At its core is Reward-Guided Privileged Self-Distillation (RPSD), a post-training framework that combines self-distillation under asymmetric information with reward-guided policy improvement. Specifically, reward-guided self-distillation progressively improves a degradation-aware privileged policy through reward-guided positive and negative targets, while privileged self-distillation transfers its preferred restoration behavior into a blind generator conditioned on the low-quality input. In this way, degradation-aware restoration knowledge and reward preference are progressively internalized into the single-step deployment model. Extensive experiments demonstrate that USIR outperforms both all-in-one and agentic restoration methods in perceptual quality and fidelity, while achieving significantly higher inference efficiency than agentic methods. Code and model weights are available at https://anonymous.4open.science/r/anonymous-744F.