# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 3 The Gated High-Low Resolution Frames Network
### Overall Structure  
As shown in Figure 2, the GHLRF network model consists of four modules: a pre-initial hidden state network (IHSNet), a gated adjacent high-low resolution frame alignment network (GAFANet), a scale local level significant feature fusion network (SLLFNet), and a reconstruction network (RNet) designed to convert low-resolution (LR) reference frames into high resolution (HR) reference frames for video super-resolution processing.  
The pre-initial hidden state network (IHSNet) is used to take the first frame in a video sequence and its neighboring frames as input and convert them into an initially hidden state feature. The gated adjacent high-low resolution frame alignment network (GAFANet) consists of three sub-modules, the feature extraction module, the stream estimation network, Spynet [18], and the gated adjacent frame high-low resolution motion compensation module, GNMCB, for the alignment of LR and HR-adjacent frames. The scale local level significant feature fusion network, SLLFNet, is used to perform scale attention fusion on the aligned features generated by (GAFANet) to enhance high-frequency details. Finally,  
Figure 2: The network overall architecture of our GHLRF. **(a)** The gated adjacent high-low resolution frame alignment network (GAFANet) consists of the residual layer RB, the optical flow estimation network Spynet, the gated high-low resolution motion compensation GHLMC, and the IHSNet is a pre-initial hidden state network. **(b)** Scale local level significant feature fusion network (SLLFNet) consists of residual layer RB and scale local level significant feature fusion network, **(c)** reconfiguration network (RNet) consists of residual unit RB, 2\(\times\) magnified sub-pixel convolution up-sampling, and convolution Conv3. Blue RB indicates a 5-layer residual block composition; green RB indicates a 1-layer residual block composition; Conv3 indicates a convolutional layer composed of a 3 \(\times\) 3 convolutional kernel as well as a Relu activation function.  
the reconstruction network (RNet) takes the fused features with the input of the reference frame that underwent double triple up-sampling to generate the HR reference frame.  
Specifically, an initial hidden state feature, H0, is first generated using a given set of L consecutive LR frames as input to IHSNet, and then alignment features are generated using LR reference frames and adjacent frames as input to GAFANet, which consists of three sub-modules: the feature extraction module, the optical flow estimation network Spynet, and the gated adjacent frames high-low resolution motion The feature extraction module extracts the shallow features of the LR reference frame and adjacent frames, which are then fed into the stream estimation network Spynet to generate the LR optical stream. Next, the shallow features of the neighboring frames, the initially hidden features, or the recursive neighboring high-resolution features and the LR optical stream are input to GNNCB, and the gating mechanism is used to determine the goodness of the features after motion compensation. Finally, the features are fed into the RNet with the reference frame that has undergone double triple up-sampling to generate the HR reference frame.