  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.
