论文标题
视频超分辨率的新型双密集连接网络
A Novel Dual Dense Connection Network for Video Super-resolution
论文作者
论文摘要
视频超分辨率（VSR）是指相应低分辨率（LR）视频的高分辨率（HR）视频的重建。最近，VSR受到了越来越多的关注。在本文中，我们提出了一个新型的双密度连接网络，该网络可以产生高质量的超分辨率（SR）结果。输入框架被创造性地分为参考框架，周期前组和颞后组，代表不同时间段的信息。这种分组方法提供了不同时间段的准确信息，而不会引起时间信息障碍。同时，我们产生了一个新的损失函数，这有助于增强模型的收敛能力。实验表明，我们的模型优于VID4数据集和SPMCS-11数据集中的其他高级模型。
Video super-resolution (VSR) refers to the reconstruction of high-resolution (HR) video from the corresponding low-resolution (LR) video. Recently, VSR has received increasing attention. In this paper, we propose a novel dual dense connection network that can generate high-quality super-resolution (SR) results. The input frames are creatively divided into reference frame, pre-temporal group and post-temporal group, representing information in different time periods. This grouping method provides accurate information of different time periods without causing time information disorder. Meanwhile, we produce a new loss function, which is beneficial to enhance the convergence ability of the model. Experiments show that our model is superior to other advanced models in Vid4 datasets and SPMCS-11 datasets.
