  Video deblurring relies on leveraging information from other frames in the
video sequence to restore the blurred regions in the current frame. Mainstream
approaches employ bidirectional feature propagation, spatio-temporal
transformers, or a combination of both to extract information from the video
sequence. However, limitations in memory and computational resources
constraints the temporal window length of the spatio-temporal transformer,
preventing the extraction of longer temporal contextual information from the
video sequence. Additionally, bidirectional feature propagation is highly
sensitive to inaccurate optical flow in blurry frames, leading to error
accumulation during the propagation process. To address these issues, we
propose \textbf{BSSTNet}, \textbf{B}lur-aware \textbf{S}patio-temporal
\textbf{S}parse \textbf{T}ransformer Network. It introduces the blur map, which
converts the originally dense attention into a sparse form, enabling a more
extensive utilization of information throughout the entire video sequence.
Specifically, BSSTNet (1) uses a longer temporal window in the transformer,
leveraging information from more distant frames to restore the blurry pixels in
the current frame. (2) introduces bidirectional feature propagation guided by
blur maps, which reduces error accumulation caused by the blur frame. The
experimental results demonstrate the proposed BSSTNet outperforms the
state-of-the-art methods on the GoPro and DVD datasets.
