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.
2018 25th IEEE International Conference on Image Processing (ICIP)