Recent studies have achieved significant advances in all-in-one adverse weather image restoration, primarily driven by the development of sophisticated model architectures. In this work, we find that effectively coordinating the complex interactions and potential optimization conflicts among different restoration tasks is also a critical factor determining the overall performance of all-in-one adverse weather image restoration models. To this end, we propose an effective all-in-one adverse weather image restoration framework, named MOE-WIRNet, designed to harmonize the learning process across various degradation types and ensure well-balanced performance among different restoration tasks. To enhance training equilibrium, we integrate a multi-task collaboration optimization strategy into the framework, coordinating the convergence dynamics of distinct restoration objectives. Furthermore, we incorporate an asymmetric mixture-of-experts (MoE) architecture into the framework to effectively address the distinct degradation patterns and varying severity levels presented by different tasks. Extensive experiments demonstrate that our framework consistently outperforms current state-of-the-art models on multiple real-world adverse weather benchmark datasets.
Chen et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: