{"ID":2872051,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09147","arxiv_id":"2509.09147","title":"JFRFFNet: A Data-Model Co-Driven Graph Signal Denoising Model with Partial Prior Information","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.","short_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 str...","url_abs":"https://arxiv.org/abs/2509.09147","url_pdf":"https://arxiv.org/pdf/2509.09147v1","authors":"[\"Ziqi Yan\",\"Zhichao Zhang\"]","published":"2025-09-11T04:43:35Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}