With the growing availability of hand-held cameras in recent years, more and more images and videos are taken at any time and any place. However, they usually suffer from undesirable blur due to camera shake or object motion in the scene. In recent years, a few modern video deblurring methods are proposed and achieve impressive performance. However, they are still not suitable for practical applications as high computational cost or using future information as input. To address the issues, we propose a sequentially one-to-one video deblurring network (SOON) which can deblur effectively without any future information. It transfers both spatial and temporal information to the next frame by utilizing the recurrent architecture. In addition, we design a novel Spatio-Temporal Attention module to nudge the network to focus on the meaningful and essential features in the past. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblurring methods, both quantitatively and qualitatively, on various challenging real-world deblurring datasets. Moreover, as our method deblurs in an online manner and is potentially real-time, it is more suitable for practical applications.

近年来，随着手持相机的普及，越来越多的图像和视频可以随时随地拍摄。然而，由于场景中的相机抖动或物体运动，它们通常会出现不希望的模糊。近年来，提出了一些现代视频去模糊方法并取得了令人印象深刻的性能。然而，它们仍然不适合实际应用，如高计算成本或使用未来信息作为输入。为了解决这些问题，我们提出了一个顺序一对一的视频去模糊网络（SOON），它可以在没有任何未来信息的情况下有效地去模糊。它通过利用循环架构将空间和时间信息传输到下一帧。此外，我们设计了一个新颖的时空注意模块来推动网络专注于过去有意义和基本的特征。大量实验表明，在各种具有挑战性的现实世界去模糊数据集上，所提出的方法在数量和质量上都优于最先进的去模糊方法。此外，由于我们的方法以在线方式去模糊并且可能是实时的，因此更适合实际应用。