  Recently, video super resolution (VSR) has become a very impactful task in
the area of Computer Vision due to its various applications. In this paper, we
propose Recurrent Back-Projection Generative Adversarial Network (RBPGAN) for
VSR in an attempt to generate temporally coherent solutions while preserving
spatial details. RBPGAN integrates two state-of-the-art models to get the best
in both worlds without compromising the accuracy of produced video. The
generator of the model is inspired by RBPN system, while the discriminator is
inspired by TecoGAN. We also utilize Ping-Pong loss to increase temporal
consistency over time. Our contribution together results in a model that
outperforms earlier work in terms of temporally consistent details, as we will
demonstrate qualitatively and quantitatively using different datasets.
