SupGAN: A General Super-Resolution GAN-Promoting Training Method

An image super-resolution (SR) method based on Generative Adversarial Networks (GANs) has achieved impressive results in terms of visual performance. However, the weights of loss functions in these methods are usually set to fixed values manually, which cannot fully adapt to different datasets and tasks, and may result in a decrease in the perceptual effect of the SR images. To address this issue and further improve visual quality, we propose a perception-driven SupGAN, which improves the generator and loss function of GAN-based image super-resolution models. The generator adopts multi-scale feature extraction and fusion to restore SR images with diverse and fine textures. We design a network-training method based on the proportion of high-frequency information in images (BHFTM), which utilizes the proportion of high-frequency information in images obtained through the Canny operator to set the weights of the loss function. In addition, we employ the four-patch method to better simulate the degradation of complex real-world scenarios. We extensively test our method and compare it with recent SR methods (BSRGAN, Real-ESRGAN, RealSR, SwinIR, LDL, etc.) on different types of datasets (OST300, 2020track1, RealWorld38, BSDS100 etc.) with a scaling factor of ×4. The results show that the NIQE metric improves, and also demonstrate that SupGAN can generate more natural and fine textures while suppressing unpleasant artifacts.