Hybrid Self-Attention Transformer U-Net for Fourier Single-Pixel Imaging Reconstruction at Low Sampling Rates

This is the authors' abstract. We don't add key points for this paper.

Fourier Single-Pixel Imaging exhibits significant advantages over conventional imaging techniques, including high interference resistance, broad spectral adaptability, nonlocal imaging capability, and long-range detection. However, in practical applications, FSPI relies on undersampling reconstruction, which inevitably leads to ringing artifacts that degrade image quality. To enhance reconstruction performance, a Transformer-based FSPI reconstruction network is proposed. The network adopts a U-shaped architecture, composed of multiple Hybrid Self-Attention Transformer Modules and Feature Fusion Modules. The experimental results demonstrate that the proposed network achieves high-quality reconstruction at low sampling rates and outperforms traditional reconstruction methods and convolutional network-based approaches in terms of both visual appearance and image quality metrics. This method holds significant potential for high-speed single-pixel imaging applications, enabling the reconstruction of high-quality images at extremely low sampling rates.