  Due to the limitations of capture devices and scenarios, egocentric videos
frequently have low visual quality, mainly caused by high compression and
severe motion blur. With the increasing application of egocentric videos, there
is an urgent need to enhance the quality of these videos through
super-resolution. However, existing Video Super-Resolution (VSR) works,
focusing on third-person view videos, are actually unsuitable for handling
blurring artifacts caused by rapid ego-motion and object motion in egocentric
videos. To this end, we propose EgoVSR, a VSR framework specifically designed
for egocentric videos. We explicitly tackle motion blurs in egocentric videos
using a Dual Branch Deblur Network (DB$^2$Net) in the VSR framework. Meanwhile,
a blurring mask is introduced to guide the DB$^2$Net learning, and can be used
to localize blurred areas in video frames. We also design a MaskNet to predict
the mask, as well as a mask loss to optimize the mask estimation. Additionally,
an online motion blur synthesis model for common VSR training data is proposed
to simulate motion blurs as in egocentric videos. In order to validate the
effectiveness of our proposed method, we introduce an EgoVSR dataset containing
a large amount of fast-motion egocentric video sequences. Extensive experiments
demonstrate that our EgoVSR model can efficiently super-resolve low-quality
egocentric videos and outperform strong comparison baselines. Our code,
pre-trained models and data can be found at https://github.com/chiyich/EGOVSR/.
