  For the success of video deblurring, it is essential to utilize information
from neighboring frames. Most state-of-the-art video deblurring methods adopt
motion compensation between video frames to aggregate information from multiple
frames that can help deblur a target frame. However, the motion compensation
methods adopted by previous deblurring methods are not blur-invariant, and
consequently, their accuracy is limited for blurry frames with different blur
amounts. To alleviate this problem, we propose two novel approaches to deblur
videos by effectively aggregating information from multiple video frames.
First, we present blur-invariant motion estimation learning to improve motion
estimation accuracy between blurry frames. Second, for motion compensation,
instead of aligning frames by warping with estimated motions, we use a pixel
volume that contains candidate sharp pixels to resolve motion estimation
errors. We combine these two processes to propose an effective recurrent video
deblurring network that fully exploits deblurred previous frames. Experiments
show that our method achieves the state-of-the-art performance both
quantitatively and qualitatively compared to recent methods that use deep
learning.
