  In real-world single image super-resolution (SISR) task, the low-resolution
image suffers more complicated degradations, not only downsampled by unknown
kernels. However, existing SISR methods are generally studied with the
synthetic low-resolution generation such as bicubic interpolation (BI), which
greatly limits their performance. Recently, some researchers investigate
real-world SISR from the perspective of the camera and smartphone. However,
except the acquisition equipment, the display device also involves more
complicated degradations. In this paper, we focus on the camera-screen
degradation and build a real-world dataset (Cam-ScreenSR), where HR images are
original ground truths from the previous DIV2K dataset and corresponding LR
images are camera-captured versions of HRs displayed on the screen. We conduct
extensive experiments to demonstrate that involving more real degradations is
positive to improve the generalization of SISR models. Moreover, we propose a
joint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) is
trained to model the degradation and produces more various of LR images, which
is validated to be efficient for data augmentation. Then the dual residual
channel attention network (DuRCAN) learns to recover the SR image. The weighted
combination of L1 loss and proposed Laplacian loss are applied to sharpen the
high-frequency edges. Extensive experimental results in both typical synthetic
and complicated real-world degradations validate the proposed method
outperforms than existing SOTA models with less parameters, faster speed and
better visual results. Moreover, in real captured photographs, our model also
delivers best visual quality with sharper edge, less artifacts, especially
appropriate color enhancement, which has not been accomplished by previous
methods.
