The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work
that is capable of generating realistic textures during single image
super-resolution. However, the hallucinated details are often accompanied with
unpleasant artifacts. To further enhance the visual quality, we thoroughly
study three key components of SRGAN - network architecture, adversarial loss
and perceptual loss, and improve each of them to derive an Enhanced SRGAN
(ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block
(RRDB) without batch normalization as the basic network building unit.
Moreover, we borrow the idea from relativistic GAN to let the discriminator
predict relative realness instead of the absolute value. Finally, we improve
the perceptual loss by using the features before activation, which could
provide stronger supervision for brightness consistency and texture recovery.
Benefiting from these improvements, the proposed ESRGAN achieves consistently
better visual quality with more realistic and natural textures than SRGAN and
won the first place in the PIRM2018-SR Challenge. The code is available at
this https URL .

该研究通过提出 ESRGAN，改进了 SRGAN 的网络架构、对抗损失和感知损失三个关键部分。ESRGAN 获得了 PIRM2018-SR 挑战的第一名，并取得了比 SRGAN 更好的视觉质量和更真实、更自然的纹理。