ProtoFlow: Universal One-step Flow Restorer via Prototype Guidance

Universal image restoration seeks to recover high-quality images from diverse degradations within a unified framework. Recent diffusion- and flow-based methods achieve strong restoration performance by leveraging powerful generative priors, yet remain costly due to multi-step computation and offer only limited control through text prompts. In addition, restoration performance deteriorates when explicit degradation information is unavailable. To address these issues, we rethink image restoration as a short-horizon transport from degraded latent toward the clean-image manifold, where the timestep naturally serves as a restoration-strength controller. We further introduce ProtoFlow, a prototype-guided one-step flow model that leverages Gaussian prototypes to enhance the representation of both degradation and semantics, thereby bridging the gap between settings with and without explicit degradation. On CoTIR-Bench, ProtoFlow significantly outperforms the CoTIR-4B on full-reference metrics, remains competitive on no-reference metrics, and runs about $7.7\times$ faster. These results demonstrate that one-step flow restoration can achieve strong performance, flexible intensity control, and high efficiency when paired with structured prototype priors.