Various blur distortions in video will cause negative impact on both human
viewing and video-based applications, which makes motion-robust deblurring
methods urgently needed. Most existing works have strong dataset dependency and
limited generalization ability in handling challenging scenarios, like blur in
low contrast or severe motion areas, and non-uniform blur. Therefore, we
propose a PRiOr-enlightened and MOTION-robust video deblurring model
(PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group
convolution to efficiently encode heterogeneous prior information, explicitly
enhancing the scenes' perception while mitigating the output's artifacts. On
the other hand, we design the priors representing blur distribution, to better
handle non-uniform blur in spatio-temporal domain. Besides the classical camera
shake caused global blurry, we also prove the generalization for the downstream
task suffering from local blur. Extensive experiments demonstrate we can
achieve the state-of-the-art performance on well-known REDS and GoPro datasets,
and bring machine task gain.

该研究提出了一种 PRiOr 启发的、适用于具有挑战性的模糊的 MOTION-robust 视频去模糊模型 (PROMOTION), 其中使用 3D 群卷积来有效编码异质先验信息，增强场景感知，同时减少输出的伪影，并设计表示模糊分布的先验，以更好地在时空域中处理非均匀模糊。经过大量实验证明，该方法可以在 RED 和 GoPro 数据集上实现最先进的性能，并带来机器任务收益。