  Self-similarity refers to the image prior widely used in image restoration
algorithms that small but similar patterns tend to occur at different locations
and scales. However, recent advanced deep convolutional neural network based
methods for image restoration do not take full advantage of self-similarities
by relying on self-attention neural modules that only process information at
the same scale. To solve this problem, we present a novel Pyramid Attention
module for image restoration, which captures long-range feature correspondences
from a multi-scale feature pyramid. Inspired by the fact that corruptions, such
as noise or compression artifacts, drop drastically at coarser image scales,
our attention module is designed to be able to borrow clean signals from their
"clean" correspondences at the coarser levels. The proposed pyramid attention
module is a generic building block that can be flexibly integrated into various
neural architectures. Its effectiveness is validated through extensive
experiments on multiple image restoration tasks: image denoising, demosaicing,
compression artifact reduction, and super resolution. Without any bells and
whistles, our PANet (pyramid attention module with simple network backbones)
can produce state-of-the-art results with superior accuracy and visual quality.
Our code will be available at
https://github.com/SHI-Labs/Pyramid-Attention-Networks
