Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
<|reference_start|>Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance: 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.<|reference_end|>
@article{fan2024driving-video,
    title={Driving-Video Dehazing with Non-Aligned Regularization for Safety
  Assistance},
    author={Junkai Fan, Jiangwei Weng, Kun Wang, Yijun Yang, Jianjun Qian, Jun Li,
  and Jian Yang},
    journal={arXiv preprint arXiv:2405.09996},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09996},
    primaryClass={cs.CV}
}