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.

本文提出了一种更复杂但实用的退化模型，其中包括随机混合的模糊，下采样和噪声退化，以达到改善单图超分辨率方法的实用性的目的。实验结果表明，这个新的退化模型可以显著改善深度超分辨率方法的实用性，为真实的单图超分辨率应用提供了一种强有力的替代解决方案。