JFRFFNet: A Data-Model Co-Driven Graph Signal Denoising Model with Partial Prior Information

Ziqi Yan; Zhichao Zhang

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arXiv · 2509.09147

JFRFFNet: A Data-Model Co-Driven Graph Signal Denoising Model with Partial Prior Information

Ziqi Yan·Zhichao Zhang

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Abstract

Wiener filtering in the joint time-vertex fractional Fourier transform (JFRFT) domain has shown high effectiveness in denoising time-varying graph signals. Traditional filtering models use grid search to determine the transform-order pair and compute filter coefficients, while learnable ones employ gradient-descent strategies to optimize them; both require complete prior information of graph signals. To overcome this shortcoming, this letter proposes a data-model co-driven denoising approach, termed neural-network-aided joint time-vertex fractional Fourier filtering (JFRFFNet), which embeds the JFRFT-domain Wiener filter model into a neural network and updates the transform-order pair and filter coefficients through a data-driven approach. This design enables effective denoising using only partial prior information. Experiments demonstrate that JFRFFNet achieves significant improvements in output signal-to-noise ratio compared with some state-of-the-art methods.

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BibTeX RIS

Ziqi Yan, Zhichao Zhang. 2025-09-11. JFRFFNet: A Data-Model Co-Driven Graph Signal Denoising Model with Partial Prior Information. https://arxiv.org/abs/2509.09147

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