一种新的用于视频超分辨率的双密集连接网络
A Novel Dual Dense Connection Network for Video Super-resolution
摘要：
视频超分辨率（VSR）是指从相应的低分辨率（LR）视频重建高分辨率（HR）视频。近年来，振动时效越来越受到人们的关注。在本文中，我们提出了一种新的双稠密连接网络，可以产生高质量的超分辨率（SR）的结果。该方法将输入帧分为参考帧、时前组和时后组，分别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，而不会造成时间信息混乱。同时，我们提出了一种新的损失函数，这有利于增强模型的收敛能力。在Vid 4数据集和SPMCS-11数据集上的实验表明，该模型的性能优于其他先进模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
