Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especially in dynamic driving scenarios with unpredictable weather conditions. In this paper, we propose a pioneering approach that addresses this challenge through a nonaligned regularization strategy. Our core concept involves identifying clear frames that closely match hazy frames, serving as references to supervise a video dehazing network. Our approach comprises two key components: reference matching and video dehazing. Firstly, we introduce a non-aligned reference frame matching module, leveraging an adaptive sliding window to match high-quality reference frames from clear videos. Video dehazing incorporates flow-guided cosine attention sampler and deformable cosine attention fusion modules to enhance spatial multiframe alignment and fuse their improved information. To validate our approach, we collect a GoProHazy dataset captured effortlessly with GoPro cameras in diverse rural and urban road environments. Extensive experiments demonstrate the superiority of the proposed method over current state-of-the-art methods in the challenging task of real driving-video dehazing. Project page.

翻译：真实驾驶视频去雾面临巨大挑战，原因在于获取精确对齐的雾霾/清晰视频对来训练有效模型存在固有困难，尤其是在动态驾驶场景及不可预测天气条件下。本文提出一种创新方法，通过非对齐正则化策略解决该挑战。其核心思想是识别与雾霾帧高度匹配的清晰帧，作为监督视频去雾网络的参考。该方法包含两个关键模块：参考帧匹配与视频去雾。首先，我们引入非对齐参考帧匹配模块，利用自适应滑动窗口从清晰视频中筛选高质量参考帧。视频去雾中融合了流引导余弦注意力采样器与可变形余弦注意力融合模块，以增强空间多帧对齐性能并整合优化信息。为验证方法有效性，我们使用GoPro相机在多样化城乡道路环境中采集了GoProHazy数据集。大量实验表明，在真实驾驶视频去雾这一挑战性任务中，所提方法性能显著优于现有最优方法。项目主页。