Untrained Graph Neural Networks for Denoising

Abstract

A fundamental problem in signal processing is to denoise a signal. While there are many well-performing methods for denoising signals defined on regular supports, such as images defined on two-dimensional grids of pixels, many important classes of signals are defined over irregular domains such as graphs. This paper introduces two untrained graph neural network architectures for graph signal denoising, provides theoretical guarantees for their denoising capabilities in a simple setup, and numerically validates the theoretical results in more general scenarios. The two architectures differ on how they incorporate the information encoded in the graph, with one relying on graph convolutions and the other employing graph upsampling operators based on hierarchical clustering. Each architecture implements a different prior over the targeted signals. To numerically illustrate the validity of the theoretical results and to compare the performance of the proposed architectures with other denoising alternatives, we present several experimental results with real and synthetic datasets.

References in corpus (5)

• Cooperative Game Theory Approaches for Network Partitioning
• Discrete Signal Processing on Graphs
• Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
• Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising
• Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators