  The success of the state-of-the-art video deblurring methods stems mainly
from implicit or explicit estimation of alignment among the adjacent frames for
latent video restoration. However, due to the influence of the blur effect,
estimating the alignment information from the blurry adjacent frames is not a
trivial task. Inaccurate estimations will interfere the following frame
restoration. Instead of estimating alignment information, we propose a simple
and effective deep Recurrent Neural Network with Multi-scale Bi-directional
Propagation (RNN-MBP) to effectively propagate and gather the information from
unaligned neighboring frames for better video deblurring. Specifically, we
build a Multi-scale Bi-directional Propagation~(MBP) module with two U-Net RNN
cells which can directly exploit the inter-frame information from unaligned
neighboring hidden states by integrating them in different scales. Moreover, to
better evaluate the proposed algorithm and existing state-of-the-art methods on
real-world blurry scenes, we also create a Real-World Blurry Video Dataset
(RBVD) by a well-designed Digital Video Acquisition System (DVAS) and use it as
the training and evaluation dataset. Extensive experimental results demonstrate
that the proposed RBVD dataset effectively improves the performance of existing
algorithms on real-world blurry videos, and the proposed algorithm performs
favorably against the state-of-the-art methods on three typical benchmarks. The
code is available at https://github.com/XJTU-CVLAB-LOWLEVEL/RNN-MBP.
