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.

翻译：视频去模糊依赖于利用视频序列中其他帧的信息来恢复当前帧的模糊区域。主流方法采用双向特征传播、时空Transformer或两者结合的方式从视频序列中提取信息。然而，内存和计算资源的限制约束了时空Transformer的时间窗口长度，使其无法从视频序列中提取更长时序的上下文信息。此外，双向特征传播对模糊帧中不准确的光流高度敏感，导致传播过程中的误差累积。为解决这些问题，我们提出\textbf{BSSTNet}，即\textbf{B}lur-aware \textbf{S}patio-temporal \textbf{S}parse \textbf{T}ransformer Network。该方法引入了模糊图，将原本稠密的注意力转换为稀疏形式，从而能够更广泛地利用整个视频序列的信息。具体而言，BSSTNet (1) 在Transformer中使用更长的时间窗口，利用更远帧的信息来恢复当前帧的模糊像素；(2) 引入由模糊图引导的双向特征传播，减少了模糊帧引起的误差累积。实验结果表明，所提出的BSSTNet在GoPro和DVD数据集上优于现有最先进的方法。