Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window. They are less efficient compared to the recurrent-based methods. In this work, we propose a novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame. It divides the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks. In addition, a hidden state adaptation module that allows the current frame to selectively use information from hidden state is introduced to enhance its robustness to appearance change and error accumulation. Extensive ablation study validate the effectiveness of the proposed modules. Experiments on several benchmark datasets demonstrate the superior performance of the proposed method compared to state-of-the-art methods on video super-resolution.

翻译：多数视频超分辨率方法在时间滑动窗口的相邻框的帮助下,超级解析一个单一的参考框架。 与经常性方法相比,它们效率较低。 在此工作中, 我们提出一个新的反复出现的视频超分辨率方法, 它在利用先前的框来超级解析当前框架方面既有效又高效。 它将输入分为由多个拟议双流结构分解区块组成的经常单元。 此外, 引入了一个隐藏状态适应模块, 允许当前框架有选择地使用隐蔽状态的信息, 以提高其外观变化和错误积累的稳健性。 广泛的关系研究验证了拟议模块的有效性。 一些基准数据集实验显示, 与视频超分辨率的最新方法相比, 拟议的方法的优劣性表现 。