MOERL: When Mixture-of-Experts Meet Reinforcement Learning for Adverse Weather Image Restoration

Bo Li, Tao Wang

Abstract: Adverse weather conditions, such as rain, snow, and haze, introduce complex degradations that present substantial challenges for effective image restoration. Existing all-in-one models often rely on fixed network structures, limiting their ability to adapt to the varying characteristics of different weather conditions. Moreover, these models typically lack the iterative refinement process that human experts use for progressive image restoration. In this work, we propose MOERL, a Mixture-of-Experts (MoE) model optimized with reinforcement learning (RL) to enhance image restoration across diverse weather conditions. Our method incorporates two core types of experts, i.e., channel-wise modulation and spatial modulation experts to address task-specific degradation characteristics while minimizing task interference. In addition, inspired by human expertise, we frame the optimization process as a sequential, progressive problem, allowing the network to refine its parameters progressively and adapt to specific weather conditions. Extensive experiments demonstrate the efficacy and superiority of our proposed method. The code and pre-trained models will be available.