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

• URL: http://arxiv.org/abs/2105.10465v1
• Date: Fri, 21 May 2021 17:02:15 GMT
• Title: Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution
• Authors: Boyan Xu and Hujun Yin
• Abstract summary: We propose a novel encoder-decoder network with added graph convolutions. Experiments show it significantly boosts performance for image restoration tasks. We believe it opens up opportunities for GCN-based approaches in more applications.
• Score: 11.531085904098003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks [0.9463895540925061]
  We introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN) It not only models the graph structure but also extracts generalized unsupervised features to enhance classification performance. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method.
  arXiv  Detail & Related papers  (2024-03-24T06:28:54Z)
• Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding [5.164875580197953]
  In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies. This paper proposes a novel graph learning framework called the graph convolutional network with self-attention (GCN-SA) The proposed scheme exhibits an exceptional generalization capability in node-level representation learning.
  arXiv  Detail & Related papers  (2024-03-06T05:00:31Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks [11.857894213975644]
  We propose a hybrid graph neural network (GNN) framework that combines traditional GCN filters with band-pass filters defined via the geometric scattering transform. Our theoretical results establish the complementary benefits of the scattering filters to leverage structural information from the graph, while our experiments show the benefits of our method on various learning tasks.
  arXiv  Detail & Related papers  (2022-01-22T00:47:41Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets [81.63035727821145]
  Spectral graph convolutional networks (SGCNs) have been attracting increasing attention in graph representation learning. We propose a novel class of spectral graph convolutional networks that implement graph convolutions with adaptive graph wavelets.
  arXiv  Detail & Related papers  (2021-08-03T17:57:53Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Directed Graph Convolutional Network [15.879411956536885]
  We extend spectral-based graph convolution to directed graphs by using first- and second-order proximity. A new GCN model, called DGCN, is then designed to learn representations on the directed graph.
  arXiv  Detail & Related papers  (2020-04-29T06:19:10Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.