Abstract
Images used in real-world applications such as image or video retrieval, outdoor surveillance, and autonomous driving suffer from poor weather conditions. When designing robust computer vision systems, removing adverse weather such as haze, rain, and snow is a significant problem. Recently, deep-learning methods offered a solution for a single type of degradation. Current state-of-the-art universal methods struggle with combinations of degradations, such as haze and rain-streak. Few algorithms have been developed that perform well when presented with images containing multiple adverse weather conditions. This work focuses on developing an efficient solution for multiple adverse weather removal using a unified quaternion neural architecture called CMAWRNet. It is based on a novel texture-structure decomposition block, a novel lightweight encoder-decoder quaternion transformer architecture, and an attentive fusion block with low-light correction. We also introduce a quaternion similarity loss function to preserve color information better. The quantitative and qualitative evaluation of the current state-of-the-art benchmarking datasets and real-world images shows the performance advantages of the proposed CMAWRNet compared to other state-of-the-art weather removal approaches dealing with multiple weather artifacts. Extensive computer simulations validate that CMAWRNet improves the performance of downstream applications such as object detection. This is the first time the decomposition approach has been applied to the universal weather removal task.
Abstract (translated)
在实际应用中，如图像或视频检索、户外监控和自动驾驶等领域使用的图片会受到恶劣天气条件的影响。设计稳健的计算机视觉系统时，去除雾气、雨水和雪等不利天气因素是一个重要的问题。最近，深度学习方法为单一类型的退化提供了解决方案，但目前最先进的通用方法在面对如雾霾与雨迹这样的多种降质组合时仍面临挑战。很少有算法能在处理包含多种恶劣天气条件的图像时表现出色。 本研究专注于开发一种高效的多不良天气去除解决方案，采用名为CMAWRNet（Color and Multi-Adverse Weather Removal Network）的统一四元数神经架构。该方法基于一个新颖的纹理结构分解块、一个轻量级编码器解码器四元数转换器架构以及一个带有低光校正功能的关注融合模块，并引入了一个四元数相似性损失函数，以更好地保留颜色信息。 当前最先进的基准数据集和实际世界图像的定量及定性评估显示，与处理多种天气效果的其他方法相比，所提出的CMAWRNet表现更优。广泛的计算机模拟验证了CMAWRNet能提升下游应用（如物体检测）的表现。这是首次将分解方法应用于通用天气去除任务中。
URL
https://arxiv.org/abs/2505.01882
