  Image super-resolution is one of the important computer vision techniques
aiming to reconstruct high-resolution images from corresponding low-resolution
ones. Most recently, deep learning-based approaches have been demonstrated for
image super-resolution. However, as the deep networks go deeper, they become
more difficult to train and more difficult to restore the finer texture
details, especially under real-world settings. In this paper, we propose a
Residual Channel Attention-Generative Adversarial Network(RCA-GAN) to solve
these problems. Specifically, a novel residual channel attention block is
proposed to form RCA-GAN, which consists of a set of residual blocks with
shortcut connections, and a channel attention mechanism to model the
interdependence and interaction of the feature representations among different
channels. Besides, a generative adversarial network (GAN) is employed to
further produce realistic and highly detailed results. Benefiting from these
improvements, the proposed RCA-GAN yields consistently better visual quality
with more detailed and natural textures than baseline models; and achieves
comparable or better performance compared with the state-of-the-art methods for
real-world image super-resolution.
