In this paper, we propose a novel joint deblurring and multi-frame
interpolation (DeMFI) framework, called DeMFI-Net, which accurately converts
blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on
flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and
recursive boosting (RB), in terms of multi-frame interpolation (MFI). The
DeMFI-Net jointly performs deblurring and MFI where its baseline version
performs feature-flow-based warping with FAC-FB module to obtain a
sharp-interpolated frame as well to deblur two center-input frames. Moreover,
its extended version further improves the joint task performance based on
pixel-flow-based warping with GRU-based RB. Our FAC-FB module effectively
gathers the distributed blurry pixel information over blurry input frames in
feature-domain to improve the overall joint performances, which is
computationally efficient since its attentive correlation is only focused
pointwise. As a result, our DeMFI-Net achieves state-of-the-art (SOTA)
performances for diverse datasets with significant margins compared to the
recent SOTA methods, for both deblurring and MFI. All source codes including
pretrained DeMFI-Net are publicly available at
this https URL

本文提出了一种新颖的联合去模糊和多帧插值（DeMFI）框架，称为 DeMFI-Net，它基于基于流引导的注意力相关特征增强（FAC-FB）模块和递归增强（RB）准确地将低帧率的模糊视频转换为高帧率的清晰视频，用于多帧插值（MFI）。DeMFI-Net 通过 FAC-FB 模块执行去模糊和 MFI，其中其基线版本通过 FAC-FB 模块执行特征流变形，以获得清晰插值帧，并去模糊两个中心输入帧。此外，其扩展版本基于基于像素流的变形和 GRU-based RB 进一步改善了联合任务性能。因此，我们的 DeMFI-Net 在各种数据集上均取得了最先进的性能，比最近的最先进方法有显著的差距，既可以去模糊也可以进行 MFI。