An MoE-Driven Unified Image Restoration Framework for Adverse Weather Conditions

Adverse natural weather conditions frequently cause substantial performance degradation in outdoor vision systems, underscoring the critical importance of research on image restoration techniques. Employing a unified set of network parameters to restore degraded images across diverse weather conditions has emerged as a key research direction in the field of image restoration. In this work, we propose MUIRF, a Mixture-of-Experts (MoE)-driven unified image restoration framework for multiple adverse weather conditions. Specifically, our technical contribution includes a novel channel-level parameter sharing strategy guided by a shallow-feature-based MoE (CPSM). This fine-grained parameter sharing strategy adaptively selects convolution weight channels for cross-task sharing based on the input image, enabling the network to accurately capture weather-general features, while the remaining channels encode weather-specific features corresponding to each weather condition. CPSM facilitates precise channel selection, thereby enhancing the robustness and accuracy of MUIRF during joint training across diverse image restoration tasks under varying weather conditions. Additionally, gradient conflicts inevitably arise in shared parameters due to the divergent optimization objectives across tasks. To address this challenge, we propose a meta-vector-guided gradient homogenization (MVGH) algorithm that mitigates inter-task gradient conflicts and improves image restoration quality. Comprehensive experimental evaluations demonstrate that our proposed network outperforms most state-of-the-art approaches, validating its superior performance and effectiveness.