Son, HyeongseokLee, JunyongCho, SunghyunLee, SeungyongUmetani, NobuyukiWojtan, ChrisVouga, Etienne2022-10-042022-10-0420221467-8659https://doi.org/10.1111/cgf.14667https://diglib.eg.org:443/handle/10.1111/cgf14667While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real-time video deblurring framework consisting of a lightweight multi-task unit that supports both video deblurring and motion compensation in an efficient way. The multi-task unit is specifically designed to handle large portions of the two tasks using a single shared network and consists of a multi-task detail network and simple networks for deblurring and motion compensation. The multi-task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real-time deblurring. Moreover, by stacking multiple multi-task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state-of-theart deblurring quality of our approach, which runs at a much faster speed compared to previous methods and show practical real-time performance (30.99dB@30fps measured on the DVD dataset).CCS Concepts: Computing methodologies --> Computational photographyComputing methodologiesComputational photography10.1111/cgf.14667177-18812 pages