DAVID: Dual-Attentional Video Deblurring
WACV 2020 | Blind video deblurring is a challenging task because the blur due to camera shake, object movement and defocusing is heterogeneous in both temporal and spatial dimensions. Traditional methods train on datasets synthesized with a single level of blur, and thus do not generalize well across levels of blurriness. To address this challenge, we propose a dual attention mechanism to dynamically aggregate temporal cues for deblurring with an end-to-end trainable network structure. Extensive ablative studies and qualitative visualizations further demonstrate the advantage of our method in handling real video blur.
Collaborators: Junru Wu, Ding Liu, Manmohan Chandraker, Zhangyang Wang