Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution

Graph convolutional networks (GCNs) have achieved great success on dealing with data of non-Euclidean structures. Their success directly attribute to effective fitting graph structures to data such as in social media and knowledge databases. For image processing applications, the use of graph structures and GCNs have not been fully explored. In this paper, we propose a novel encoder-decoder network with added graph convolutions by converting feature maps to vertexes of a pre-generated graph to synthetically construct graph-structured data. By doing this, we inexplicitly apply graph Laplacian regularization to the feature maps, making them more structured. The experiments show it significantly boosts performance for the task of image restoration, including deblurring and super-resolution. We believe that it opens up opportunities for GCN-based approaches in many applications.