Physics-Informed Frequency-Domain Pixel Super-Resolution for Fourier Single-Pixel Detection

Fourier single-pixel detection (FSPD) suffers from an inherent trade-off between spatial resolution and detection efficiency. This constraint significantly impacts measurement precision in optical instrumentation applications. To overcome these measurement limitations, we propose a frequency-domain super-resolution network based on physical measurement principles tailored for FSPD instruments. This method decomposes the frequency domain into magnitude and phase branches with targeted optimization strategies based on Fourier imaging distribution characteristics, while leveraging the physical sparse measurement priors of FSPD frequency sampling to enhance detection accuracy and measurement noise suppression. The network utilizes residual dense blocks for low-frequency measurement stability and combines self-attention mechanisms with non-local blocks for high-frequency detail detection. By integrating measurement consistency constraints and physics-informed frequency weighting based on FSPD’s inherent sampling characteristics, the proposed approach achieves superior measurement reconstruction accuracy. The physical measurement priors in the frequency domain are used to constrain the structural consistency of the super-resolution detection process. A multi-loss function with physical measurement constraints balances spectral fidelity and measurement consistency. Experiments demonstrate that under 128×128 resolution under-sampling detection, we achieved 256×256 resolution high-accuracy measurements. Moreover, at the same resolution, high-precision detection has been achieved for challenging measurement scenarios, such as A4 paper reflective media, which are difficult to reconstruct with traditional FSPD instruments, advancing practical measurement applications including precision microscopy detection and scattering measurement systems.