  It is widely acknowledged that single image super-resolution (SISR) methods
would not perform well if the assumed degradation model deviates from those in
real images. Although several degradation models take additional factors into
consideration, such as blur, they are still not effective enough to cover the
diverse degradations of real images. To address this issue, this paper proposes
to design a more complex but practical degradation model that consists of
randomly shuffled blur, downsampling and noise degradations. Specifically, the
blur is approximated by two convolutions with isotropic and anisotropic
Gaussian kernels; the downsampling is randomly chosen from nearest, bilinear
and bicubic interpolations; the noise is synthesized by adding Gaussian noise
with different noise levels, adopting JPEG compression with different quality
factors, and generating processed camera sensor noise via reverse-forward
camera image signal processing (ISP) pipeline model and RAW image noise model.
To verify the effectiveness of the new degradation model, we have trained a
deep blind ESRGAN super-resolver and then applied it to super-resolve both
synthetic and real images with diverse degradations. The experimental results
demonstrate that the new degradation model can help to significantly improve
the practicability of deep super-resolvers, thus providing a powerful
alternative solution for real SISR applications.
