# Video Super-Resolution with Regional Focus for Recurrent Network
## 2 Related Work
[34] proposed a deformable 3D convolutional network to integrate spatio-temporal information in video super-resolution, which has excellent spatio-temporal modeling and flexible motion perception ability. [PERSON] et al. [35] designed a separate non-local module to explore the relationship between video frames, effectively fuse video frames, and capture the relationship between feature maps via channel attention residual blocks. [PERSON] et al. [36] introduced deformable convolution and deformable pooling in the model, used an additional offset to increase the spatial sampling location in the module, and learned the target task offset without additional supervision. The temporal variant alignment network proposed by [PERSON] et al. [37] aligns the reference frame and each corresponding frame adaptively at the feature level without calculating the optical flow. Compared with the explicit motion compensation method, these implicit alignment methods reduce the computational cost, but in these methods, the redundant computation of multiple adjacent frames still exists in a temporal window, which requires the caching of multiple frames in advance to achieve video super-resolution. Compared with the alignment methods, there are another class of methods that do not use frame alignment to achieve video super-resolution. Such methods mainly use spatial or spatio-temporal information for feature extraction, where the 2D convolution method extracts the feature information of video frames in space, and the 3D convolution method extracts the temporal information in the spatio-temporal domain to consider the correlation between video frames. Here, recurrent neural network methods are very suitable for processing video sequences with temporal information because of their recursive characteristics. [PERSON] et al. [38] proposed an end-to-end trainable video frame and video super-resolution network about feature context correlation that is composed of local and context networks. The fast spatio-temporal residual network proposed by [PERSON] et al. [39] uses 3D convolution for video super-resolution tasks, which improves performance while maintaining a low computational load. [PERSON] et al. [40] proposed a deep neural network with dual subnets and multistage communication up-sampling for the super-resolution of large motion video. [PERSON] et al. [41] proposed an end-to-end reversible residual spatio-temporal network, which takes full advantage of spatio-temporal information from low-resolution to high-resolution, and effectively modeled the temporal uniformity of continuous video frames. There are also some methods to consider video super-resolution problems from another perspective. [PERSON] et al. [42] expanded the super-resolution reconstruction magnification from the traditional \(\times\)4 to the high magnification of \(\times\)16 and \(\times\)32. [PERSON] et al. [43] completed the final video super-resolution task by taking different video frames taken by the telephoto, wide-angle, and ultra wide cameras as mutual references. The head pose estimation methods [44; 45] proposed by [PERSON] et al. also have some enlightenment and reference significances. To sum up, the non-alignment methods mainly rely on the nonlinear ability of the neural network to learn the motion correlation between video frames, thereby achieving the super-resolution reconstruction of video frames. The reconstruction result of the network is not affected by whether the video frames are aligned or not, and the design of the network structure determines the learning ability of the network. Therefore, the reasonable design of the network structure is helpful for improving the performance of video super-resolution. The method proposed in this paper increases the feature information involved in the reconstruction through the feature extraction from shallow layer to deep layer, and the attention of different network layers to different feature regions.