  Graph convolutional networks (GCNs) have achieved great success in dealing
with data of non-Euclidean structures. Their success directly attributes to
fitting graph structures effectively 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 that it significantly boosts performance for image restoration
tasks, including deblurring and super-resolution. We believe it opens up
opportunities for GCN-based approaches in more applications.
