TL;DR
WeatherRemover is a lightweight, multi-weather image restoration model that balances high-quality enhancement with efficiency using a UNet-like structure, gating mechanisms, and multi-scale vision Transformers.
Contribution
It introduces a novel multi-weather removal model with a gating mechanism and multi-scale Transformer, achieving efficient and effective image restoration across various weather conditions.
Findings
Achieves superior restoration quality across multiple weather scenarios.
Balances performance with parameter efficiency and computational cost.
Source code available at https://github.com/RICKand-MORTY/WeatherRemover.
Abstract
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…
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