Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions. In order to utilize neighboring sharp patches, typical methods rely mainly on homography or optical flows to spatially align neighboring blurry frames. However, such explicit approaches are less effective in the presence of fast motions with large pixel displacements. In this work, we propose a novel implicit method to learn spatial correspondence among blurry frames in the feature space. To construct distant pixel correspondences, our model builds a correlation volume pyramid among all the pixel-pairs between neighboring frames. To enhance the features of the reference frame, we design a correlative aggregation module that maximizes the pixel-pair correlations with its neighbors based on the volume pyramid. Finally, we feed the aggregated features into a reconstruction module to obtain the restored frame. We design a generative adversarial paradigm to optimize the model progressively. Our proposed method is evaluated on the widely-adopted DVD dataset, along with a newly collected High-Frame-Rate (1000 fps) Dataset for Video Deblurring (HFR-DVD). Quantitative and qualitative experiments show that our model performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.

本文提出一种新的视频去模糊方法，利用特征空间中的模糊帧学习空间对应关系，通过构建像素之间的相关性体金字塔来构建远程像素对应，使用相关聚合模块最大化邻居与参考帧的像素对相关性来增强参考帧特征，然后将聚合后的特征馈入重构模块以获取恢复的帧。我们设计了一种生成对抗法来逐步优化模型，对广泛采用的DVR数据集和一个新收集的高帧速(1000 fps)视频去模糊数据集进行了评估。定量和定性实验显示，我们的模型在两个数据集上均表现比先前最先进的方法更好，证明了模型所有范围空间对应关系对于视频去模糊的好处。

ARVo: 学习视频去模糊时的全尺度体积对应