面向安全辅助的非对齐正则化驾驶视频去雾
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
AI总结：
本文针对真实驾驶视频去雾中难以获取精确对齐训练对的问题，提出基于非对齐参考帧匹配与流引导注意力融合的视频去雾方法，并构建GoProHazy数据集验证其优越性。
摘要：
真实驾驶视频去雾面临重大挑战，因为获取精确对齐的有雾/清晰视频对以进行有效模型训练本身就很困难，尤其是在天气条件不可预测的动态驾驶场景中。本文提出了一种开创性方法，通过非对齐正则化策略来解决这一挑战。我们的核心思想是识别与有雾帧高度匹配的清晰帧，将其作为参考来监督视频去雾网络。我们的方法包含两个关键组成部分：参考匹配和视频去雾。首先，我们引入了一个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匹配高质量参考帧。视频去雾结合了流引导的余弦注意力采样器和可变形余弦注意力融合模块，以增强空间多帧对齐并融合其改进后的信息。为验证我们的方法，我们收集了一个GoProHazy数据集，该数据集使用GoPro相机在多样的乡村和城市道路环境中轻松采集。大量实验表明，所提方法在真实驾驶视频去雾这一具有挑战性的任务上优于当前最先进的方法。项目主页。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
