  Super-resolution is a fundamental problem in computer vision which aims to
overcome the spatial limitation of camera sensors. While significant progress
has been made in single image super-resolution, most algorithms only perform
well on synthetic data, which limits their applications in real scenarios. In
this paper, we study the problem of real-scene single image super-resolution to
bridge the gap between synthetic data and real captured images. We focus on two
issues of existing super-resolution algorithms: lack of realistic training data
and insufficient utilization of visual information obtained from cameras. To
address the first issue, we propose a method to generate more realistic
training data by mimicking the imaging process of digital cameras. For the
second issue, we develop a two-branch convolutional neural network to exploit
the radiance information originally-recorded in raw images. In addition, we
propose a dense channel-attention block for better image restoration as well as
a learning-based guided filter network for effective color correction. Our
model is able to generalize to different cameras without deliberately training
on images from specific camera types. Extensive experiments demonstrate that
the proposed algorithm can recover fine details and clear structures, and
achieve high-quality results for single image super-resolution in real scenes.
