# Video Super-Resolution with Regional Focus for Recurrent Network
###### Abstract  
Video super-resolution reconstruction is the process of reconstructing low-resolution video frames into high-resolution video frames. Most of the current methods use motion estimation and motion compensation to extract temporal series information, but the inaccuracy of motion estimation will lead to the degradation of the quality of video super-resolution results. Additionally, when using convolution network to extract feature information, the number of feature information is limited by the number of feature channels, resulting in poor reconstruction results. In this paper, we propose a recurrent structure of regional focus network for video super-resolution, which can avoid the influence of inaccurate motion compensation on super-resolution results. Meanwhile, regional focus blocks in the network can focus on different areas of video frames, extract different features from shallow to deep layers, and skip-connect to the last layer of the network through feature aggregation to improve the richness of features participating in the reconstruction. The experimental results show that our method has higher computational efficiency and better video super-resolution results than other temporal modeling methods. recurrent neural network; regional focus; feature aggregation; video super-resolution. +
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alignment more accurate, so as to improve the reconstruction quality. [PERSON] et al. [14] learned the gradient mapping function between high-resolution and low-resolution to regularize multi-frame fusion, and used forward and backward motion field priori to regularize the estimation of interframe motion flow, which can restore better details with less information. Deformable convolution is the most common implicit motion compensation method. As its typical example, [PERSON] et al. [10] uses a deformable alignment module and a spatio-temporal attention fusion module to solve the large motion problem and efficient fusion of multiple frames in video super-resolution, respectively. Different from alignment methods, non-aligned methods [15; 16; 17] do not perform frame alignment before video super-resolution reconstruction, and mainly utilize spatial information or spatio-temporal information to extract features, such as 3D convolution, recurrent neural network, and non-local method. The non-local operation of method [18] is to calculate the similarity between other pixels in the image and the pixel at the current position, where the response value of the current position is equal to the sum of the weights of all possible positions in the input feature map. [PERSON] et al. [19] adopts the structure of cascaded residual blocks in the recurrent network, which reconstructs rich texture details while avoiding the vanishing gradient problem. The cascaded residual structure [20] has achieved a good effect in processing single-image super-resolution. However, when applied to the video super-resolution, this structure does not take into account the unbalanced proportion between simple and complex areas of the video frame. Usually, most of the video frames are simple regions, which makes the optimization direction of the network dominated by simple regions during the training process, resulting in the deterioration of the reconstruction results quality and the blurring of local regions. In addition, because the convolutional network has the characteristics of local connection and global sharing, the super-resolution task based on the convolutional network needs to restore the detailed information of the reconstruction results point-by-point. The features extracted from different residual blocks in the residual cascade network have the characteristics of progressive refinement. The output features of different residual blocks are similar, and the feature diversity is limited by the number of feature channels, which affects the recovery of high-frequency details in complex regions. Motivated by the above discussion, a recursive regional focus network for video super-resolution will be proposed in this paper.