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.
翻译:图表共变网络(GCNs)在处理非欧元结构数据方面取得了巨大成功, 其成功直接归功于将图表结构有效地与社会媒体和知识数据库等数据相匹配。 对于图像处理应用程序,尚未充分探索图形结构和GCNs的使用。 在本文中, 我们提议建立一个新颖的编码器- 解码网络, 通过将地貌图转换成预生成的图形的顶端, 合成构建图形结构化数据, 增加图形共变。 通过这样做, 我们明确地将图解正规化应用于地貌地图, 使其结构化。 实验显示, 它极大地提升了图像恢复任务的性能, 包括分流和超分辨率。 我们相信, 它为GCN在更多应用中开辟了机会。