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.

在这篇论文中，我们提出了一种名为Recurrent Back-Projection Generative Adversarial Network (RBPGAN)的视频超分辨率算法，旨在生成在保留空间细节的同时具有时间上连贯性的解决方案。RBPGAN集成了两种最先进的模型，以在不降低视频生成准确性的同时获得最佳效果。模型的生成器受到了RBPN系统的启发，而鉴别器受到了TecoGAN的启发。我们还利用Ping-Pong loss来增加时间上的一致性。通过使用不同数据集进行定性和定量的实验，我们的贡献使得该模型在时间上具有一致的细节方面优于早期的研究成果。

RBPGAN：视频超分辨率的循环反投影GAN