  Video super-resolution (VSR) has become one of the most critical problems in
video processing. In the deep learning literature, recent works have shown the
benefits of using adversarial-based and perceptual losses to improve the
performance on various image restoration tasks; however, these have yet to be
applied for video super-resolution. In this work, we propose a Generative
Adversarial Network(GAN)-based formulation for VSR. We introduce a new
generator network optimized for the VSR problem, named VSRResNet, along with a
new discriminator architecture to properly guide VSRResNet during the GAN
training. We further enhance our VSR GAN formulation with two regularizers, a
distance loss in feature-space and pixel-space, to obtain our final
VSRResFeatGAN model. We show that pre-training our generator with the
Mean-Squared-Error loss only quantitatively surpasses the current
state-of-the-art VSR models. Finally, we employ the PercepDist metric (Zhang et
al., 2018) to compare state-of-the-art VSR models. We show that this metric
more accurately evaluates the perceptual quality of SR solutions obtained from
neural networks, compared with the commonly used PSNR/SSIM metrics. Finally, we
show that our proposed model, the VSRResFeatGAN model, outperforms current
state-of-the-art SR models, both quantitatively and qualitatively.
