Fourier single-pixel imaging for dynamic color scene reconstruction via joint priors

Single-pixel imaging enables high-sensitivity measurements across diverse spectral regimes. However, reconstructing high-quality dynamic color scenes under sub-Nyquist sampling remains challenging due to the trade-off between spatiotemporal resolution and reconstruction fidelity. This article proposes a dynamic color Fourier single-pixel imaging framework that integrates a multi-channel acquisition scheme with a joint-prior reconstruction model to address this challenge. A hyper-Laplacian prior is employed to exploit cross-channel correlations, while a local low-rank prior captures temporal redundancy in dynamic scenes. Compared to a single prior, the joint approach enables mutual reinforcement between the two priors. Based on this formulation, we derive an efficient alternating minimization scheme that achieves significantly accelerated convergence through closed-form solutions. Experimental results demonstrate that the proposed method yields a PSNR improvement of over 5 dB compared to conventional Fourier single-pixel imaging, while maintaining robust reconstruction within 92 ms per frame.