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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.09147 |
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| _version_ | 1866916946293817344 |
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| author | Yan, Ziqi Zhang, Zhichao |
| author_facet | Yan, Ziqi Zhang, Zhichao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JFRFFNet: A Data-Model Co-Driven Graph Signal Denoising Model with Partial Prior Information Yan, Ziqi Zhang, Zhichao Signal Processing 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. |
| title | JFRFFNet: A Data-Model Co-Driven Graph Signal Denoising Model with Partial Prior Information |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.09147 |