  Real-world image super-resolution (SR) is a challenging image translation
problem. Low-resolution (LR) images are often generated by various unknown
transformations rather than by applying simple bilinear down-sampling on
high-resolution (HR) images. To address this issue, this paper proposes a novel
pipeline which exploits style and attention mechanism in real-world SR. Our
pipeline consists of a style Variational Autoencoder (styleVAE) and a SR
network incorporated with attention mechanism. To get real-world-like
low-quality images paired with the HR images, we design the styleVAE to
transfer the complex nuisance factors in real-world LR images to the generated
LR images. We also use mutual information estimation (MI) to get better style
information. For our SR network, we firstly propose a global attention residual
block to learn long-range dependencies in images. Then another local attention
residual block is proposed to enforce the attention of SR network moving to
local areas of images in which texture detail will be filled. It is worth
noticing that styleVAE can be presented in a plug-and-play manner and thus can
help to improve the generalization and robustness of our SR method as well as
other SR methods. Extensive experiments demonstrate that our method surpasses
the state-of-the-art work, both quantitatively and qualitatively.
