Weighted median autoregressive graph filters

Shaodian Liu; Wenxu Yan; Wei Shuai; Wenyuan Wang

doi:10.1117/12.3026302

9 February 2024 Weighted median autoregressive graph filters

Shaodian Liu, Wenxu Yan, Wei Shuai, Wenyuan Wang

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730O (2024) https://doi.org/10.1117/12.3026302
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

The graph filter can extract the desired features from the graph signal and filter out the noise signal. Most of the graph filters proposed in the literature are linear. Autoregressive moving average (ARMA) filter is a polynomial filter. Compared to finite-impulse response (FIR) graph filters, ARMA graph filters are robust to changes in the signal and/or graph, but are still linear. In this work, we propose a weighted median autoregressive graph filter (WMAF) based on a first-order ARMA graph filter. The proposed filter is a combination of weighted median filter in the traditional signal processing field and median autoregressive filters (MAF), and can be implemented in a distributed way. Compared with linear filter and MAF, the proposed WMAF filter has better filtering effect on pulse noise. In the denoising application of real sensor network data set, the filtered signal has a better signal-to-noise ratio.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaodian Liu, Wenxu Yan, Wei Shuai, and Wenyuan Wang "Weighted median autoregressive graph filters", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730O (9 February 2024); https://doi.org/10.1117/12.3026302

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KEYWORDS

Tunable filters

Digital filtering

Signal filtering

Linear filtering

Optical filters

Signal processing

Electronic filtering

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