Super-resolution (SR) is the technique of increasing the nominal resolution of image / video content accompanied with quality improvement. Video super-resolution (VSR) can be considered as the generalization of single image super-resolution (SISR). This generalization should be such that more detail is created in the output using adjacent input frames. In this paper, we propose a grouped residual in residual network (GRRN) for VSR. By adjusting the hyperparameters of the proposed structure, we train three networks with different numbers of parameters and compare their quantitative and qualitative results with the existing methods. Although based on some quantitative criteria, GRRN does not provide better results than the existing methods, in terms of the quality of the output image it has acceptable performance.

翻译：超分辨率（SR）是一种在提升图像/视频内容标称分辨率的同时改善其质量的技术。视频超分辨率（VSR）可视为单图像超分辨率（SISR）的推广，这种推广应能利用相邻输入帧在输出中生成更多细节。本文提出了一种用于VSR的分组残差内残差网络（GRRN）。通过调整所提出结构的超参数，我们训练了三种参数数量不同的网络，并将其定量与定性结果与现有方法进行了比较。尽管在某些定量指标上GRRN并未优于现有方法，但从输出图像质量来看，其性能是可接受的。