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Adverse weather removal tasks like deraining, desnowing, and dehazing areusually treated as separate tasks. However, in practical autonomous drivingscenarios, the type, intensity,and mixing degree of weather are unknown, sohandling each task separately cannot deal with the complex practical scenarios.In this paper, we study the blind adverse weather removal problem.Mixture-of-Experts (MoE) is a popular model that adopts a learnable gate toroute the input to different expert networks. The principle of MoE involvesusing adaptive networks to process different types of unknown inputs.Therefore, MoE has great potential for blind adverse weather removal. However,the original MoE module is inadequate for coupled multiple weather types andfails to utilize multi-scale features for better performance. To this end, wepropose a method called Weather-aware Multi-scale MoE (WM-MoE) based onTransformer for blind weather removal. WM-MoE includes two key designs:WEather-Aware Router (WEAR) and Multi-Scale Experts (MSE). WEAR assigns expertsfor each image token based on decoupled content and weather features, whichenhances the model's capability to process multiple adverse weathers. To obtaindiscriminative weather features from images, we propose Weather GuidanceFine-grained Contrastive Learning (WGF-CL), which utilizes weather clusterinformation to guide the assignment of positive and negative samples for eachimage token. Since processing different weather types requires differentreceptive fields, MSE leverages multi-scale features to enhance the spatialrelationship modeling capability, facilitating the high-quality restoration ofdiverse weather types and intensities. Our method achieves state-of-the-artperformance in blind adverse weather removal on two public datasets and ourdataset. We also demonstrate the advantage of our method on downstreamsegmentation tasks.

WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal