  Blind image deblurring is a fundamental and challenging computer vision
problem, which aims to recover both the blur kernel and the latent sharp image
from only a blurry observation. Despite the superiority of deep learning
methods in image deblurring have displayed, there still exists major challenge
with various non-uniform motion blur. Previous methods simply take all the
image features as the input to the decoder, which handles different degrees
(e.g. large blur, small blur) simultaneously, leading to challenges for sharp
image generation. To tackle the above problems, we present a deep two-branch
network to deal with blurry images via a component divided module, which
divides an image into two components based on the representation of blurry
degree. Specifically, two component attentive blocks are employed to learn
attention maps to exploit useful deblurring feature representations on both
large and small blurry regions. Then, the blur-aware features are fed into
two-branch reconstruction decoders respectively. In addition, a new feature
fusion mechanism, orientation-based feature fusion, is proposed to merge sharp
features of the two branches. Both qualitative and quantitative experimental
results show that our method performs favorably against the state-of-the-art
approaches.
