# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 2 Related Works  
In recent years, with the rise of deep learning, various types of deep learning-based methods have been applied in the field of computer vision. For example, [PERSON] et al. [15] proposed SRCNN, which is known as a pioneering work by introducing a convolutional neural network entirely. The network used three convolutional layers to represent different non-linear mapping relationships for reconstruction, and, although the network layers were shallow, they showed good performance at the time compared to traditional methods. Since then, a large number of deep learning-based methods have started to flood into super-resolution reconstruction techniques [16; 17; 18; 19; 20; 21; 22; 23].  
[PERSON] et al. [1] were the first to propose an end-to-end convolutional neural network-based video super-resolution approach Real-time video super-resolution with spatiotemporal networks and motion compensation (VESPCN), which is based on the single-image super-resolution algorithm, ESPCN, is combined with a motion estimation module. [PERSON] et al. [6] proposed a motion estimation network (deep video super-resolution using HR optical flow estimation, SOFVSR), which uses a coarse-to-fine method to estimate the high-resolution optical flow between frames. The algorithm uses a three-layer pyramid structure to estimate the optical flow by progressively up-sampling the final high-resolution HR optical flow and then down-sampling the number of channels to expand the low-resolution LR optical flow. A basic architecture network video restoration with enhanced deformable convolutional networks (EDVR) was proposed by [PERSON] et al. [17], which super-segmented the video into four main modules: feature extraction, alignment, aggregation, and reconstruction. Motion estimation is performed using PCD pyramidal variability convolution, followed by the corresponding spatiotemporal feature combination in combination with the TSA attention module.  
[PERSON] et al. [7] proposed an attention mechanism approach to reconstruct spatiotemporal information features by using a dual-attention network. [PERSON] et al. [24] proposed a progressive fusion strategy approach that uses a non-local approach to inter-frame similarity determination and then makes full use of spatiotemporal information through a stepwise separation and merging approach, greatly improving model performance. [PERSON] et al. [2] proposed the search for essential components in video and super-resolution, beyond BasicVSR, a two-way loop-based network, and designed a baseline framework according to which the corresponding network can be changed, such as alignment and aggregation, which uses the two-way loop The Omniscient video super-resolution (OVSR), proposed by [PERSON] et al. [25], is an omniscient framework that can be changed by simply satisfying Equations (1) and (2) of their paper. The network uses the two-way loop idea, combined with the two-branch idea to achieve it and can be used for past, present, and future information so that the reconstructed frame obtains more feature information. [PERSON] et al. [21] proposed a one-way loop-based lightweight network (revisiting temporal modeling for video super-resolution,RRN) and proposed a temporal aggregation network that combines past information and low-resolution video frames using a one-way loop mechanism for fusion and feature refinement through the idea of residuals, which is simple and efficient.  
Good results are achieved in all of these approaches, with the use of aligned versus non-aligned approaches. Inspired by these approaches, this paper adopts a video super-resolution framework that displays aligned versus non-full loop, with adaptive motion compensation by looping the retained high-resolution features with low-resolution features, enhancing the fusion of locally salient features in a non-full-loop framework and reducing the problem of unbalanced one-way loop output results.