DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution
This addresses image quality issues in super-resolution for applications like photography and vision, but it is incremental as it builds on existing GAN-based methods.
The paper tackles the problem of artifacts in GAN-based single image super-resolution by proposing DifAugGAN, a diffusion-style data augmentation method that improves discriminator calibration, resulting in superior performance over state-of-the-art methods on synthetic and real-world datasets.
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.