Video deblurring methods, aiming at recovering consecutive sharp frames from a given blurry video, usually assume that the input video suffers from consecutively blurry frames. However, in real-world blurry videos taken by modern imaging devices, sharp frames usually appear in the given video, thus making temporal long-term sharp features available for facilitating the restoration of a blurry frame. In this work, we propose a video deblurring method that leverages both neighboring frames and present sharp frames using hybrid Transformers for feature aggregation. Specifically, we first train a blur-aware detector to distinguish between sharp and blurry frames. Then, a window-based local Transformer is employed for exploiting features from neighboring frames, where cross attention is beneficial for aggregating features from neighboring frames without explicit spatial alignment. To aggregate long-term sharp features from detected sharp frames, we utilize a global Transformer with multi-scale matching capability. Moreover, our method can easily be extended to event-driven video deblurring by incorporating an event fusion module into the global Transformer. Extensive experiments on benchmark datasets demonstrate that our proposed method outperforms state-of-the-art video deblurring methods as well as event-driven video deblurring methods in terms of quantitative metrics and visual quality. The source code and trained models are available at https://github.com/shangwei5/STGTN.

翻译：视频去模糊方法旨在从给定的模糊视频中恢复连续的清晰帧，通常假设输入视频连续出现模糊帧。然而，在现代成像设备拍摄的真实模糊视频中，清晰帧往往会出现，从而使得长程时间上的清晰特征可用于促进模糊帧的恢复。本文提出了一种视频去模糊方法，通过混合Transformer实现相邻帧与现有清晰帧的特征聚合。具体而言，我们首先训练一个模糊感知检测器以区分清晰帧与模糊帧。随后，采用基于窗口的局部Transformer从相邻帧中提取特征，其中交叉注意力机制无需显式空间对齐即可有效聚合相邻帧特征。为从检测到的清晰帧中聚合长程清晰特征，我们使用具有多尺度匹配能力的全局Transformer。此外，通过向全局Transformer中集成事件融合模块，我们的方法可轻松扩展至事件驱动视频去模糊领域。在基准数据集上的大量实验表明，所提方法在定量指标与视觉质量上均优于现有最先进的视频去模糊方法及事件驱动视频去模糊方法。源代码与训练模型已开源至https://github.com/shangwei5/STGTN。