DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based
  Single Image Super-resolution
Axi Niu Kang Zhang Joshua Tian Jin Tee Trung X. Pham Jinqiu Sun Chang D. Yoo In So Kweon Yanning Zhang
It is well known the adversarial optimization of GAN-based image
super-resolution (SR) methods makes the preceding SR model generate unpleasant
and undesirable artifacts, leading to large distortion. We attribute the cause
of such distortions to the poor calibration of the discriminator, which hampers
its ability to provide meaningful feedback to the generator for learning
high-quality images. To address this problem, we propose a simple but
non-travel diffusion-style data augmentation scheme for current GAN-based SR
methods, known as DifAugGAN. It involves adapting the diffusion process in
generative diffusion models for improving the calibration of the discriminator
during training motivated by the successes of data augmentation schemes in the
field to achieve good calibration. Our DifAugGAN can be a Plug-and-Play
strategy for current GAN-based SISR methods to improve the calibration of the
discriminator and thus improve SR performance. Extensive experimental
evaluations demonstrate the superiority of DifAugGAN over state-of-the-art
GAN-based SISR methods across both synthetic and real-world datasets,
showcasing notable advancements in both qualitative and quantitative results.