  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
https://github.com/JihyongOh/DeMFI.
