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/.

翻译：由于拍摄设备和场景的限制，第一人称视频常因高压缩率和严重运动模糊而导致低视觉质量。随着第一人称视频应用的日益增长，亟需通过超分辨率技术提升此类视频质量。然而，现有视频超分辨率（VSR）方法聚焦于第三人称视角视频，实际上难以处理第一人称视频中因快速自我运动与物体运动产生的模糊伪影。为此，我们提出EgoVSR——一种专为第一人称视频设计的VSR框架。我们通过在VSR框架中引入双分支去模糊网络（DB$^2$Net）显式处理运动模糊，同时引入模糊掩码引导DB$^2$Net学习，并用于定位视频帧中的模糊区域。我们进一步设计了MaskNet以预测掩码，并提出掩码损失函数优化掩码估计。此外，针对通用VSR训练数据，提出一种在线运动模糊合成模型，用于模拟第一人称视频中的运动模糊。为验证所提方法的有效性，我们构建了包含大量快速运动第一人称视频序列的EgoVSR数据集。大量实验表明，EgoVSR模型能够高效提升低质量第一人称视频的分辨率，并显著优于强对比基线。代码、预训练模型及数据详见https://github.com/chiyich/EGOVSR/。