# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 3 The Gated High-Low Resolution Frames Network
### Gate-Guided Adjacent High-Low Resolution Frame Alignment Network  
The gated adjacent high-low resolution frame alignment GAFANet network is the core module that aims to solve the problems of existing methods that do not make full use of the high-low resolution adjacent frame information and the large motion estimation errors. The core idea of the model is to introduce a gating mechanism while using the high- and low-resolution neighboring frame information in an adaptive manner for motion compensation. As shown in Figure 1a, it consists of three sub-modules: the feature extraction module, the optical flow estimation module Spynet, and the gated adjacent high-low resolution frame motion compensation module GNMCB. The input information is passed through GAFANet to generate aligned video frame features \(\mathrm{f}^{\mathrm{A}}\):  
\[\mathrm{f}^{\mathrm{A}}_{1}=\mathrm{GAFANet}\left(((\mathrm{I}^{\mathrm{L}}_{ \mathrm{t-1}},\mathrm{I}^{\mathrm{L}}_{\mathrm{1}},\mathrm{H}0),\Theta_{ \mathrm{GAFA}})\ \mathrm{n}=1\right. \tag{2}\]  
\[\mathrm{f}^{\mathrm{A}}_{n}=\mathrm{GAFANet}\left(((\mathrm{I}^{\mathrm{L}}_{ \mathrm{t-1}},\mathrm{I}^{\mathrm{L}}_{\mathrm{t}},\mathrm{h}_{\mathrm{t-1}}), \Theta_{\mathrm{GAFA}})\ \mathrm{n}\geq 2\right. \tag{3}\]  
where \(\mathrm{f}^{\mathrm{A}}_{1}\) and \(\mathrm{f}^{\mathrm{A}}_{n}\) are aligned video frame features \(\mathrm{f}^{\mathrm{A}}\), \(\mathrm{I}^{\mathrm{L}}_{1}\) is denoted as the low-resolution reference of the first frame, \(\mathrm{I}^{\mathrm{L}}_{\mathrm{t}}\) is the low-resolution reference frame after the second frame, \(\mathrm{I}^{\mathrm{L}}_{\mathrm{t-1}}\) is denoted as its corresponding low-resolution neighbor frame, and \(\mathrm{h}_{\mathrm{t-1}}\) is the loop-based high-resolution frame after the first frame, \(\Theta_{\mathrm{GAFA}}\), denoted as the parameter set. Specifically, the model first performs feature extraction through the feature extraction module, then uses motion estimation methods to obtain motion information between neighboring frames and then uses the neighboring frame information to perform gated motion compensation. The gating mechanism is used to adaptively select useful information in high- and low-resolution neighboring frames to avoid the effects of motion estimation errors, thereby acquiring aligned video frames. 1. Feature extraction module  
The feature extraction module consists of five weight-sharing residual layers, and the generated features are used as input to the motion estimation network Spynet. 2. Optical flow estimation module Spynet  
The optical flow estimation module Spynet is an open-source convolutional neural network-based optical flow estimation method. Its main feature is that it can learn the mapping from image to optical flow directly, avoiding the problems of traditional optical flow algorithms where parameters need to be designed and tuned manually. In video super-resolution reconstruction tasks, Spynet is commonly used to perform optical flow estimation on low-resolution reference frames to obtain motion information between adjacent frames, further aiding in the generation of high-resolution super-resolution reconstruction results. 3. Gated adjacent high-low resolution frame motion compensation module  
Inaccurate or large errors in motion estimation can lead to inaccurate alignment of video frames and affect subsequent super-resolution reconstruction. In order to be able to adaptively select useful information for high-low resolution adjacent frames and avoid the effects of motion estimation errors. we propose a gated motion compensation module for high-low resolution adjacent frames. As shown in Figure 4, the module consists of two sub-modules, the adjacent high-low resolution frame feature extraction module and the gated motion compensation module. In this module, the gated motion compensation module is used to control the weighting of adjacent frames in terms of high-low resolution features to select useful information and achieve a more refined motion compensation.