Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and temporal upsampling. A broad range of research attempted to recover clean frames from blurred image sequences or temporally upsample frames by interpolation, yet there are very limited studies handling both problems jointly. In this work, we present a novel framework for deblurring, interpolating and extrapolating sharp frames from a motion-blurred video in an end-to-end manner. We design our framework by first learning the pixel-level motion that caused the blur from the given inputs via optical flow estimation and then predict multiple clean frames by warping the decoded features with the estimated flows. To ensure temporal coherence across predicted frames and address potential temporal ambiguity, we propose a simple, yet effective flow-based rule. The effectiveness and favorability of our approach are highlighted through extensive qualitative and quantitative evaluations on motion-blurred datasets from high speed videos.

中文翻译：

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运动模糊的视频插值和外推

摄像机或场景中物体的突然运动会导致视频模糊，因此，要恢复高质量的视频，需要两种类型的增强：视觉增强和时间上采样。广泛的研究试图通过内插从模糊的图像序列或时间上采样的帧中恢复干净的帧，但是很少有研究共同处理两个问题。在这项工作中，我们提出了一种新颖的框架，用于以端到端的方式从运动模糊的视频中对清晰的帧进行去模糊，内插和外推。我们通过首先通过光流估计来学习导致给定输入模糊的像素级运动，然后通过将解码特征与估计的流扭曲来预测多个干净帧来设计框架。为了确保跨预测帧的时间连贯性并解决潜在的时间歧义，我们提出了一个简单而有效的基于流的规则。通过对高速视频中运动模糊的数据集进行广泛的定性和定量评估，突出了我们方法的有效性和可取性。