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

Wiener filtering in the joint time–vertex fractional Fourier transform (JFRFT) domain has shown high effectiveness in denoising time-varying graph signals. Traditional filtering model uses grid search to determine the transform order pair and compute filter coefficients, while the learnable one employs gradient descent strategy to optimize them, both requiring 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 filtering model into the neural network and updates the transform order pair and filter coefficients through 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-arts.