Towards All-in-One Image Restoration: A Sparse Mixture-of-Experts Framework with Task-Adaptive Routing

Image restoration aims to reconstruct high-quality visual content from degraded observations caused by blur, noise, haze, or resolution loss. While recent deep learning approaches have achieved remarkable performance on individual restoration tasks, most existing methods still follow a task-specific paradigm that trains separate models for different degradations. This design limits their practicality in real-world scenarios where degradations are often unknown, mixed or spatially varying. In this paper, we propose MoERestore, a unified image restoration framework that integrates task-adaptive sparse mixture-of-experts routing with degradation-aware conditioning. The proposed architecture dynamically routes each input to a small subset of lightweight experts according to degradation characteristics, enabling task-adaptive capacity allocation while alleviating cross-task interference. To provide explicit guidance for restoration under ambiguous or mixed degradations, we introduce a degradation-aware module that leverages a vision-language model to generate semantic descriptions of corruption patterns. These descriptions are encoded as conditioning signals to guide the restoration process. In addition, a high-frequency feature extraction branch is incorporated to enhance fine-grained texture reconstruction. Extensive experiments on super resolution, deblurring, dehazing, and denoising benchmarks demonstrate that MoERestore achieves strong and consistent performance across diverse restoration scenarios and generalizes well to challenging real-world degradations