Signal denoising is an important problem with a vast literature. Recently, signal denoising on graphs has received a lot of attention due to the increasing use of graph-structured signals. However, well-etablished signal denoising methods do not generalize to graph signals with irregular structures, while existing graph denoising methods do not capture well the abstract representations inherent in the signals. To bridge this gap, we propose to use graph convolutional neural network with a Kron-reduction-based pooling operator for denoising on graphs. The proposed model can effectively capture the irregular data structure and learn the underlying representations in the signals, leading to improved performance over existing methods in experiments involving real-world traffic signals.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Pages0-5
Number of pages5
Publication statusAccepted/In press - 12 May 2020
Event2020 IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020

Conference

Conference2020 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20

ID: 49317376