NeXtSRGAN: enhancing super-resolution GAN with ConvNeXt discriminator for superior realism

Abstract

Deep learning technologies have significantly advanced the field of single-image super-resolution (SISR), yet existing methods often prioritize peak signal-to-noise ratio (PSNR) over visual quality and realism. In this study, we propose NeXtSRGAN, which integrates a ConvNeXt-based discriminator to overcome these limitations and achieve more realistic and high-quality super-resolution (SR) images. NeXtSRGAN enhances image realism through its novel discriminator structure and generator residual block design, leveraging a relativistic discriminator and residual scaling. Experimental results on benchmark datasets demonstrate that NeXtSRGAN surpasses existing methods, including enhanced SRGAN (ESRGAN), with an average PSNR improvement of over 1.58 dB and an SSIM enhancement of over 0.05. Notably, NeXtSRGAN exhibits exceptional performance in facial image SR, as confirmed by the KID-F metric. By focusing on perceptual quality rather than solely PSNR, NeXtSRGAN sets a new standard for image restoration and holds promise for applications in various domains, such as video surveillance, medical imaging, and satellite photos. This code is available at https://github.com/PomKlementieff/NeXtSRGAN.