# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
###### Abstract
The reconstruction achieves the purpose of restoring LR images through feature extraction and non-linear mapping. Later, because of the current work, many excellent image SR algorithms were proposed from the perspective of the network input form, network structure, loss function, and utilization degree of information, including Fast Super-Resolution by CNN (FSRCNN) [6], Very Deep Super Resolution (VDSR) [7], Efficient Sub-Pixel Convolutional Neural Network (ESPCN) [8], Enhanced Deep Residual Networks (EDSR) [9], Residual Dense Network (RDN) [10], Residual Channel Attention Networks (RCAN) [11], Dual Regression Networks (DRN) [12], etc. The development of the image super-resolution algorithm usually accompanies the development of the video SR algorithm. After the image SR technology based on deep learning is proposed, the corresponding video SR algorithm is also put forward successively, including Deep Draft-Ensemble Learning (Deep-DE) [13] and Video Super-Resolution with Convolutional Neural Networks (VSRnet) [14]. SRCNN improves the VSRnet method and uses three convolution layers, and the input is changed from the original single image to a multi-frame continuous image. After this, more and more excellent algorithms have been proposed, including Video Efficient Sub-Pixel Convolutional Neural Network (VESPCN) [15], Dynamic Upsampling Filters (DUF) [16], Detail-Revealing Deep Video Super-Resolution (DRVSR) [17], Temporally Deformable Alignment Network (TDAN) [18], Recurrent Back-Projection Network (RBPN) [19], Video Restoration With Enhanced Deformable Convolutional Networks (EDVR) [20], etc. At present, the focus of video SR algorithm research is on how to make more effective use of information between the video frames, as far as possible, to capture the target frame SR helpful information, such as high-frequency details, and therefore put forward a variety of strategies, including 3D convolution, non-local network, deformable convolution, and optical flow-based Motion Estimation and Motion Compensation (MEMC) technology. Among the once popular flow-based approaches, the estimated optical flow motion information needs to be more accurate due to its application limitation, thus affecting the subsequent SR process, ultimately resulting in performance limitations. Interframe utilization based on deformable convolution has attracted much attention recently due to its excellent performance, including EDVR, Video Enhancement and Super-Resolution Network (VESRnet) [21], and TDAN. This is because the way based on deformable convolution is the integration of multiple optical flows. If it is a convolution kernel, it is the result of the nine optical streams achieving complementary integration with each other, while the optical flow-based alignment mode is based on the particular case of the deformable convolution with the convolution kernel of 11; thus, more accurate interframe information can be obtained based on deformable convolution so that the performance will be even better. This theoretical explanation is elaborated on in [22]. In addition, some scholars migrate knowledge from other fields to SR problems, such as semantic segmentation and generating adversarial networks, and the proposed algorithms include Super-Resolution Generative Adversarial Network (SRGAN) [23], Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) [24], Video Super-Solution Residual Neural Network (VSRResNet) [25], Temporal Coherence Generative Adversarial Networks (TecoGAN) [26], etc. [PERSON] et al. [27] proposed a channel and spatial attention module (CSAM) dedicated to SISR to pay attention to the image details and different semantic levels in the feature map. [PERSON] et al. [28] proposed CNN-based asymmetric image Super Resolution network (ACNet), which highlights local key features through an asymmetric convolution structure composed of 3 \(\times\) 1, 1 \(\times\) 3, and 3 \(\times\) 3 convolutions, reducing information redundancy and accelerating training speed. At present, there is a lot of work.