For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation between video frames to aggregate information from multiple frames that can help deblur a target frame. However, the motion compensation methods adopted by previous deblurring methods are not blur-invariant, and consequently, their accuracy is limited for blurry frames with different blur amounts. To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames. First, we present blur-invariant motion estimation learning to improve motion estimation accuracy between blurry frames. Second, for motion compensation, instead of aligning frames by warping with estimated motions, we use a pixel volume that contains candidate sharp pixels to resolve motion estimation errors. We combine these two processes to propose an effective recurrent video deblurring network that fully exploits deblurred previous frames. Experiments show that our method achieves the state-of-the-art performance both quantitatively and qualitatively compared to recent methods that use deep learning.

翻译：视频混凝土的成功是利用相邻框架信息的关键。 多数最先进的视频混凝土方法都采用视频框架之间的运动补偿方法,将多个框架的信息汇总起来,从而帮助确定目标框架。 但是,以往的混凝土方法所采用的运动补偿方法并不模糊,因此,其准确性对模糊框架和不同模糊度的模糊框架来说是有限的。 为了缓解这一问题,我们提出了两种新颖的方法,通过有效地汇总多个视频框架的信息来消除这种模糊视频。 首先,我们介绍了模糊的变异运动估计学习方法,以提高模糊框架之间的运动估计准确性。 其次,关于运动补偿,我们使用一个像素量,而不是通过与估计运动的扭曲来调整框架。 我们把这两个程序结合起来,提出一个有效的经常性的视频混凝土网络,充分利用了以往的模糊框架。 实验表明,我们的方法在数量上和质量上都达到了与最近使用的深度学习方法相比的状态。