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[bibtex]@InProceedings{Fan_2024_CVPR, author = {Fan, Junkai and Weng, Jiangwei and Wang, Kun and Yang, Yijun and Qian, Jianjun and Li, Jun and Yang, Jian}, title = {Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {26109-26119} }
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
Abstract
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.
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