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.

翻译：近期，视频超分辨率（VSR）因其广泛的应用而成为计算机视觉领域中一项极具影响力的任务。本文提出了一种用于VSR的循环反向投影生成对抗网络（RBPGAN），旨在生成时间一致性解决方案的同时保留空间细节。RBPGAN融合了两种最先进的模型，以在不牺牲生成视频精度的前提下实现两者优势互补。该模型的生成器受RBPN系统启发，判别器则源自TecoGAN。我们还引入了Ping-Pong损失函数以增强时间一致性。综合来看，我们的贡献使得模型在时间一致性细节方面优于先前工作，并通过不同数据集从定性和定量角度进行了验证。