SLAIR: Structured Latent Flow Matching for All-in-One Image Restoration

Image restoration aims to recover high-quality images fromdegraded observations, yet existing methods struggle with diverse degra-dation types. In this work, we propose SLAIR, an all-in-one image restora-tion framework integrating latent space separation and deterministictransport. We introduce a latent-space separation approach that ensuresstable, distinct representations of degraded and clean images, facilitat-ing effective flow matching. By learning continuous transport flow in alatent space via an ordinary differential equation (ODE), our methodenables deterministic restoration without relying on degradation labelsor stochastic sampling. While the restoration is performed in the la-tent space, a high-frequency refinement module is introduced into thelatent decoder to handle detail distortion during decoding. Experimentsdemonstrate that SLAIR outperforms existing approaches in terms ofboth perceptual quality and computational efficiency, making it a robustsolution for scalable, degradation-agnostic image restoration. Our projectpage is available at https://fightforql.github.io/SLAIR-page.