Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution

Recent research on image super-resolution (SR) has shown that the use of perceptual losses such as feature-space loss functions and adversarial training can greatly improve the perceptual quality of the resulting SR output. In this paper, we extend the use of these perceptual-focused approaches for image SR to that of video SR. We design a 15-block residual neural network, VSRResNet, which is pre-trained on a the traditional mean -squared -error (MSE) loss and later fine-tuned with a feature-space loss function in an adversarial setting. We show that our proposed system, VSRRes-FeatGAN, produces super-resolved frames of much higher perceptual quality than those provided by the MSE-based model.