Video Super Resolution with Generative Adversarial Network

This paper exhibits a novel implementation of video super resolution through Generative adversarial network. Generative adversarial networks (GANs) are a class of artificial intelligence algorithm implemented by a system of two neural networks provide a unique way to learn deep representations. The results of GAN shows slightly lower PSNR compared to traditional methods, since the frames produced by GAN is more appealing when viewed by human. GAN can learn from large datasets and automatically add high-frequency details and features to frames while traditional methods can't. The generator discriminator architecture in GAN pushes it to generate more realistic and appealing frames.