  Blind video deblurring restores sharp frames from a blurry sequence without
any prior. It 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. Specifically, an internal attention module adaptively selects the
optimal temporal scales for restoring the sharp center frame. An external
attention module adaptively aggregates and refines multiple sharp frame
estimates, from several internal attention modules designed for different blur
levels. To train and evaluate on more diverse blur severity levels, we propose
a Challenging DVD dataset generated from the raw DVD video set by pooling
frames with different temporal windows. Our framework achieves consistently
better performance on this more challenging dataset while obtaining strongly
competitive results on the original DVD benchmark. Extensive ablative studies
and qualitative visualizations further demonstrate the advantage of our method
in handling real video blur.
