Graph Signal Denoising Via Unrolling Networks

Siheng Chen, Yonina C Eldar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to the graph domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals. We train the graph unrolling networks through unsupervised learning, where the input noisy graph signals are used to supervise the networks. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input noisy graph signals, instead of manually choosing signal priors. To validate the proposed methods, we conduct extensive experiments on both real-world datasets and simulated datasets, and demonstrate that our methods have smaller denoising errors than conventional denoising algorithms and state-of-the-art graph neural networks. For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around 40% and 60% lower than that of graph Laplacian denoising and graph wavelets, respectively.
Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
ISBN (Electronic)9781728176055
StatePublished - 6 Jun 2021
EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Toronto, ON, Canada
Duration: 6 Jun 202111 Jun 2021


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)


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