  In this paper, we propose a residual non-local attention network for
high-quality image restoration. Without considering the uneven distribution of
information in the corrupted images, previous methods are restricted by local
convolutional operation and equal treatment of spatial- and channel-wise
features. To address this issue, we design local and non-local attention blocks
to extract features that capture the long-range dependencies between pixels and
pay more attention to the challenging parts. Specifically, we design trunk
branch and (non-)local mask branch in each (non-)local attention block. The
trunk branch is used to extract hierarchical features. Local and non-local mask
branches aim to adaptively rescale these hierarchical features with mixed
attentions. The local mask branch concentrates on more local structures with
convolutional operations, while non-local attention considers more about
long-range dependencies in the whole feature map. Furthermore, we propose
residual local and non-local attention learning to train the very deep network,
which further enhance the representation ability of the network. Our proposed
method can be generalized for various image restoration applications, such as
image denoising, demosaicing, compression artifacts reduction, and
super-resolution. Experiments demonstrate that our method obtains comparable or
better results compared with recently leading methods quantitatively and
visually.
