Abstract. Generative adversarial networks (GANs) have recently been
adopted to single image super-resolution (SISR) and showed impressive
results with realistically synthesized high-frequency textures. However,
the results of such GAN-based approaches tend to include less meaningful high-frequency noise that is irrelevant to the input image. In this
paper, we propose a novel GAN-based SISR method that overcomes the
limitation and produces more realistic results by attaching an additional
discriminator that works in the feature domain. Our additional discriminator encourages the generator to produce structural high-frequency
features rather than noisy artifacts as it distinguishes synthetic and real
images in terms of features. We also design a new generator that utilizes
long-range skip connections so that information between distant layers
can be transferred more effectively. Experiments show that our method
achieves the state-of-the-art performance in terms of both PSNR and
perceptual quality compared to recent GAN-based methods