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：近年来，视频超分辨率（VSR）因其广泛的应用而成为计算机视觉领域极具影响力的研究方向。本文提出了一种用于视频超分辨率的循环反向投影生成对抗网络（RBPGAN），旨在生成保持空间细节的同时实现时间连贯的解。RBPGAN融合了两种最先进的模型，在不影响生成视频准确性的前提下兼顾两者的优势。其生成器受RBPN系统启发，而判别器则借鉴了TecoGAN的设计。此外，我们利用Ping-Pong损失函数来增强时间一致性。综合而言，我们的贡献所得模型在时间连贯细节方面优于先前工作，我们将通过不同数据集进行定性与定量展示。