Structure-Resonant Discriminator for Image Super-Resolution

Convolutional neural networks are data models. Their design should embrace the structural properties of the data being modeled, e.g., the natural images. We argue that this also holds for discriminators of adversarial training frameworks for photo-realistic image restoration. We develop this idea to highlight three essential structural features of natural images: translation equivariance, rotation invariance, and hierarchy of scale. The analysis leads to a new discriminator, Structure-Resonant Discriminator (SRD), which can capture image structures in need. The proposed SRD is demonstrated in the perceptual single image super-resolution task. By replacing only the discriminator, our method restores more visually pleasing high-resolution images than the previous state-of-the-art techniques, while exhibits the least distortions.