Video Super Resolution Using a Selective Edge Aggregation Network

Abstract

An edge map is a feature map representing the contours of the object in the image. There was a Single Image Super Resolution (SISR) method using the edge map, which achieved a notable SSIM performance improvement. Unlike SISR, Video Super Resolution (VSR) uses video, which consists of consecutive images with temporal features. Therefore, some VSR models adopted motion estimation and motion compensation to apply spatio-temporal feature maps. Unlike the models above, we tried a different method by adding edge structure information and its related post-processing to the existing model. Our model “Video Super Resolution Using a Selective Edge Aggregation Network (SEAN)” consists of a total of two stages. First, the model selectively generates an edge map using the target frame and also the neighboring frame. At this stage, we adopt the magnitude loss function so that the output of SEAN more clearly learns the contours of each object. Second, the final output is generated using the refinement (post-processing) module. SEAN shows more distinct object contours and better color correction compared to other existing models.

Keywords

• computer vision
• video super resolution
• edge detection