论文标题
实时视频通过轻量运动补偿造
Real-Time Video Deblurring via Lightweight Motion Compensation
论文作者
论文摘要
尽管运动补偿大大提高了视频质量的质量，但单独执行运动补偿和视频Deblurring需要大量的计算开销。本文提出了一个实时视频DeBlurring框架，该框架由轻巧的多任务单元组成，该单元以有效的方式支持视频脱张和运动补偿。多任务单元是专门设计的，用于使用单个共享网络处理两个任务的大部分，并由多任务详细网络和简单的网络组成，用于消除和运动补偿。多任务单元最大程度地减少了将运动补偿纳入视频DeBlurring的成本，并实现了实时脱毛。此外，通过堆叠多个多任务单元，我们的框架可以在成本和过度质量之间的灵活控制。我们通过实验性地验证了方法的最先进的质量，与以前的方法相比，该方法的运行速度要快得多，并显示了实时的实时性能（在DVD数据集中测得的30.99db@30fps）。
While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real-time video deblurring framework consisting of a lightweight multi-task unit that supports both video deblurring and motion compensation in an efficient way. The multi-task unit is specifically designed to handle large portions of the two tasks using a single shared network, and consists of a multi-task detail network and simple networks for deblurring and motion compensation. The multi-task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real-time deblurring. Moreover, by stacking multiple multi-task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state-of-the-art deblurring quality of our approach, which runs at a much faster speed compared to previous methods, and show practical real-time performance (30.99dB@30fps measured in the DVD dataset).
