Though many attempts have been made in blind super-resolution to restore
low-resolution images with unknown and complex degradations, they are still far
from addressing general real-world degraded images. In this work, we extend the
powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN),
which is trained with pure synthetic data. Specifically, a high-order
degradation modeling process is introduced to better simulate complex
real-world degradations. We also consider the common ringing and overshoot
artifacts in the synthesis process. In addition, we employ a U-Net
discriminator with spectral normalization to increase discriminator capability
and stabilize the training dynamics. Extensive comparisons have shown its
superior visual performance than prior works on various real datasets. We also
provide efficient implementations to synthesize training pairs on the fly.

本研究将 ESRGAN 应用于实际的纹理恢复应用中，在处理复杂的真实世界退化图像时，引入高阶退化建模过程，并考虑到常见的环绕和过冲伪影，采用具有谱归一化的 U-Net 鉴别器来增强鉴别器的能力和稳定训练动力学， 并提供了实时生成训练样本的高效实现。