Semantic Super-Resolution via Self-Distillation and Adversarial Learning

Abstract

Semantic super-resolution (SR) is an approach that improves the SR performance by leveraging semantic information about the scene. This study develops a novel semantic SR method that is based on the generative adversarial network (GAN) framework and self-distillation. A discriminator is adversarially trained along with a generator to extract semantic features from images and distinguish semantic differences between images. To train the generator, an additional adversarial loss is computed from the discriminator's outputs of SR images belonging to the same category and minimized via self-distillation. This guides the generator to learn implicit category-specific semantic priors. We conducted experiments for SR of text and face images using the Enhanced Deep Super-Resolution (EDSR) generator and the SRGAN discriminator. Experimental results showed that our method can contribute to improving both the quantitative and qualitative quality of SR images. Although the improvement varied depending on image category and dataset, the peak signal-to-noise ratio (PSNR) value increased by up to 0.87 dB and the perceptual index (PI) decreased by up to 0.17 by using our method. Our method outperformed existing semantic SR methods.