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Tsinghua AIBaiyunPortNorthwest China Research Institute of ElectronicNYUSanda UniversityShenzhen MSU-BIT UniversityShenzhen Research Institute of Big DatayUQZhejiang college of Security TechnologyApr 8, 2026arXiv:2604.06623The paper introduces WeatherRemover, a UNet-like architecture with gating mechanisms and a multi-scale pyramid vision Transformer, to remove rain, snow, and fog from images. It uses channel-wise attention and linear spatial reduction to optimize feature extraction and reduce computational costs. The model achieves a balance between restoration quality, parameter efficiency, and computational overhead, outperforming other multi-weather models.
WeatherRemover achieves state-of-the-art multi-weather image restoration while drastically reducing computational cost, proving that high performance doesn't have to break the bank.
Photographs taken in adverse weather conditions often suffer from blurriness, occlusion, and low brightness due to interference from rain, snow, and fog. These issues can significantly hinder the performance of subsequent computer vision tasks, making the removal of weather effects a crucial step in image enhancement. Existing methods primarily target specific weather conditions, with only a few capable of handling multiple weather scenarios. However, mainstream approaches often overlook performance considerations, resulting in large parameter sizes, long inference times, and high memory costs. In this study, we introduce the WeatherRemover model, designed to enhance the restoration of images affected by various weather conditions while balancing performance. Our model adopts a UNet-like structure with a gating mechanism and a multi-scale pyramid vision Transformer. It employs channel-wise attention derived from convolutional neural networks to optimize feature extraction, while linear spatial reduction helps curtail the computational demands of attention. The gating mechanisms, strategically placed within the feed-forward and downsampling phases, refine the processing of information by selectively addressing redundancy and mitigating its influence on learning. This approach facilitates the adaptive selection of essential data, ensuring superior restoration and maximizing efficiency. Additionally, our lightweight model achieves an optimal balance between restoration quality, parameter efficiency, computational overhead, and memory usage, distinguishing it from other multi-weather models, thereby meeting practical application demands effectively. The source code is available at https://github.com/RICKand-MORTY/WeatherRemover.
