Multi-feature prior-guided enhanced single-pixel imaging

Single-pixel imaging technology has demonstrated unique advantages in weak light detection and special spectral sensing fields. However, its reconstruction performance is limited at low measurement rates, which remains a key challenge. Current mainstream reconstruction processes face issues such as the decoupling and underutilization of measurement information at low rates, as well as the loss of sample diversity caused by deterministic mappings, thereby restricting the ability to capture fine details in complex scenes. To address these challenges, this study proposes a dual-stage reconstruction framework with multi-feature prior fusion, WFD-SPI, which achieves deep analysis of measurement information and feature reconstruction optimization through innovative architectural design. Specifically, in the conditional generative pre-training phase, the Moore-Penrose generalized inverse operator is introduced to construct the pseudoinverse space of the measurement matrix, establishing a bidirectional mapping relationship between measurement information and image information, effectively extracting latent feature representations. In the inverse diffusion reconstruction process, the Fourier frequency domain features and wavelet multi-scale space features are non-linearly fused innovatively, and a differentiable operator constraint is applied to generate the joint probability distribution of the generated image and real data, significantly enhancing the representation of high-frequency textures and edge information. Experimental results show that our method outperforms existing SPI techniques in terms of recovery quality at low measurement rates. Subsequent ablation further validates the effectiveness of our approach.