  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.
