# Video Super Resolution Using a Selective Edge Aggregation Network
## 1 Introduction  
The VSR task is usually processed using information from consecutive frames. To use this information, some VSR models [1; 2] adopted motion estimation and motion compensation to apply spatio-temporal features. However, in datasets that expresses large-scale motion, accurate motion estimation is challenging. On the other hand, edge maps can be generated through the widely used Canny edge detection, providing intuitive and structural information to the model. Therefore, we propose Stage 1 of our model as follows by adding structure information and a loss function to the existing model.  
Before generating an edge map, it is necessary to detect the motion difference between neighboring frames. Although most of the spatio-temporal information between the consecutive frames is similar, information differences exist if some objects in the frame show large-scale motion.  
In the case of a neighboring frame with little change in motion, there is no significant difference in the edge maps obtained in each frame. However, in the case of a frame with a large change in motion, there is a big difference. Therefore, we use the following three methods to obtain the edge map used in our model.  
Detect blur frame. The variance of the frame calculated using the Laplacian filter can express the degree of blur. The larger the variance, the greater the degree of change in the pixel value, which means that there are many areas with a clear contour of the object on the frame. Our model considers the variance of the frame to be a blur frame if it is lower than the threshold. Therefore, our model uses only clear frames to extract an edge map.  
Edge map extraction. Our model uses Canny edge detection to generate an edge map of each frame, except blur frames. The final edge map used in the model is generated by fusing the edge map of the target frame and the edge map of the neighboring frame one-to-one.  
Magnitude loss function. During the training, our model uses a Sobel mask to obtain the gradient magnitude corresponding to each output of the Ground Truth (GT) and the output of our model. Using the magnitude loss function, which minimizes the difference in the magnitude between those, our model trains the contours of GT. Because artifacts exist in some frames of the TestSet, such as Vid4 [3], incomplete areas are generated. To solve this problem, there has been an attempt to use correlation between frames [4]. However, these attempts also require accurate calculations such as flow estimation. So, we use an additional module to solve this problem.  
Refinement. The refinement process receives the output of Stage 1 and shows qualitatively and quantitatively improved results. This process looks similar to the refining process in the classic vision field called post-processing, but it has a different character. First, it uses deep learning, and in particular, it is possible to minimize information loss, a classic problem of convolution operation. Second, the edge and color on the output image can be processed at the same time, and it is quantitatively excellent because it tries to get as close to the GT image as possible between learning. Third, the proposed refinement was applied to the VSR (Video Super Resolution) field for the first time, and since it was designed with high versatility, it is easily compatible with other VSR models according to the user's choice.