# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 3 The Gated High-Low Resolution Frames Network
### Scale Local Level Significant Feature Fusion Network  
SLLFNet is a key part of GHLRF, a network used to fuse features from aligned video frames and extract high-frequency detailed information from the edges and textures of the video frames to improve the reconstruction quality of the video. As shown in Figure 5, the SLLFNet consists of multiple residual blocks and a U-shaped structure, where each structure introduces a multi-scale fusion module to exploit feature extraction and information fusion  
Figure 4: Schematic diagram of the gated adjacent high-low resolution frame motion compensation network (GHLMC).  
at multiple scales. In this paper, a secondary cascade of SLLFNet is adopted to obtain the final fused SLLFNet, possessing different levels of high-frequency detail features U:  
\[\text{U}=\text{SLLFNet}\;(\text{SLLFNet}\;(\text{f}^{\text{A}},\Theta_{\text{ SLLFNet}}),\Theta_{\text{SLLFNet}}) \tag{8}\]  
\[\text{SLLFNet}=[\text{U}_{1},\text{U}_{2},\text{U}_{3}] \tag{9}\]  
\[\text{U}_{1}=\text{RMB}_{1}\;(\text{RB}\;(\text{f}^{\text{A}})) \tag{10}\]  
\[\text{U}_{2}=\text{HHM}\;\text{AB}\;(\text{down}\;(\text{U}_{1})) \tag{11}\]  
\[\text{U}_{3}=\text{RMB}_{2}\;(\text{up}\;(\text{U}_{2})) \tag{12}\]  
where SLLFNet \((\cdot)\) is denoted as a local scaled hierarchical significant feature fusion network, \(\Theta_{\text{SLLFNet}}\) is the corresponding set of network model parameters, \([\text{U}_{1},\text{U}_{2},\text{U}_{3}]\) are denoted as \(\text{RMB}_{1}\;(\cdot)\), \(\text{HHMAB}\;(\cdot)\), and \(\text{RMB}_{2}\;(\cdot)\), respectively, composed as SLLFNet, \(\text{RMB}_{1}\;(\cdot)\) and \(\text{RMB}_{2}\;(\cdot)\) are residual multilevel blocks, and \(\text{HHMAB}\;(\cdot)\) is a hierarchical mixed multi-attentive block, down \((\cdot)\) is down-sampling, and up \((\cdot)\) as up-sampling.  
The residual multilevel block, as shown in Figure 6, consists of three layers for cascading and channel number splicing for each convolutional layer output to form multiple features with different levels of perceptual fields, convolutional dimensionality reduction operation for its fused features, and, finally, global residual concatenation with the input features to obtain the RMB output features.  
SLLFNet also introduces the hierarchical hybrid multi-attention block (HHMAB) at the bottom layer of the U-shaped structure, as shown in Figure 7, to further control the feature flow at different scales effectively and recover high-frequency detail  
Figure 5: Schematic diagram of a scale local level significant feature fusion network (SLLFNet). RB denotes a residual cell composed of 5 layers, RMB denotes residual multilevel block, and HHMAB denotes hierarchical hybrid multi-attention block.  
Figure 6: Schematic diagram of residual multilevel blocks (RMB). Conv3 denotes a convolutional layer consisting of \(3\times 3\) convolutional kernels and the Relu activation function, Conv1 denotes the convolution of \(1\times 1\) convolutional kernels, C denotes the number of channels spliced, and \(\oplus\) denotes the corresponding sum of elements.  
and textures. The hierarchical hybrid multi-attention block, HHMAB, convolves the feature maps by convolution kernels at different scales, after which channel attention is applied to the features obtained at different scales [26] to select the importance of features at different levels. Then, these features at different scales are weighted and fused to capture contextual spatial information by spatial attention [27]. Finally, the features at different levels of importance are selected and element-wise summed with capturing contextual spatial features to obtain detailed information at different levels of high frequency. This plug-and-play scale attention mechanism can enhance the feature extraction and information fusion capability of the network as well as enhance high-frequency detailed information, thus improving the quality of video reconstruction.