Successful video deblurring relies on effectively using sharp pixels from other frames to recover the blurry pixels of the current frame. However, mainstream methods only use estimated optical flows to align and fuse features from adjacent frames without considering the pixel-wise blur levels, leading to the introduction of blurry pixels from adjacent frames. Furthermore, these methods fail to effectively exploit information from the entire input video. To address these limitations, we propose STDANet++, which redesigns the state-of-the-art method STDANet by introducing patch-based spatio-temporal deformable attention (PSTDA) module and long-term frame fusion (LTFF) module to the BiRNN-based structure. By effectively utilizing sharp information across the entire video, the proposed method outperforms state-of-the-art methods on the GoPro, DVD and BSD datasets, according to our experimental results. The source code is available at https://github.com/huicongzhang/STDANetPP.

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 BSSTNet, Blur-aware Spatio-temporal Sparse Transformer 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, lever-aging 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 out-performs the state-of-the-art methods on the GoPro and DVD datasets.