Expert Weaving: Marrying Masked AutoRegressive and Diffusion Models for Unified Image Restoration

All-in-one image restoration aims to handle diverse degra-dations within a unified framework. While Mixture-of-Experts (MoE)architectures offer a scalable path, existing designs predominantly relyon vanilla end-to-end fidelity-oriented experts that lack task-adaptivegenerative priors, limiting their ability to recover realistic structuresand textures in complex scenarios. In this paper, we first investigatecomplementary task-adaptive generative priors: Masked Auto-Regressive(MAR) experts are tailored for local degradations (e.g., shadow) by re-constructing semantic structures via clean local backgrounds; conversely,Diffusion experts excel at global degradations (e.g., noise) by recoveringtextural details through iterative denoising. Motivated by these insights,we propose Mixture-of-Synergy-Experts (MoSE), a novel framework thatdeeply integrates these complementary generative priors into the MoEarchitecture and synergizes MAR and Diffusion experts with vanillabackbones at both image and feature levels. MoSE is orchestrated by aDINOv3-guided semantic-aware router that adaptively regulates not onlyexpert coordination but also generation control: mask sizes are adjustedto handle varying scales of local semantic corruption, while iterationsteps are modulated according to degradation intensity. By synergistically"weaving" complementary experts, our approach achieves a controllablefidelity-perception trade-off. Comprehensive experiments demonstrate theeffectiveness of MoSE, achieving competitive performance across variousall-in-one image restoration benchmarks.