Hessian sparsity-constrained self-supervised network for near-infrared single-photon single-pixel imaging

Abstract

Near-infrared (NIR) imaging has emerged as an important technology for night vision, remote sensing, and biological imaging, yet conventional array-detector-based systems are often limited by insufficient sensitivity, high cost, and substantial dark noise. Single-pixel imaging (SPI) offers an attractive alternative, enabling single-photon-level NIR imaging by using a cost-effective single-element detector. Nevertheless, SPI remains restricted by photon noise, leading to degraded imaging quality and limited frame rate under extremely low photon flux conditions. Here, we present a Hessian sparsity-constrained self-supervised network (HS3N) for single-photon NIR SPI, which can suppress noise and enable high-fidelity and real-time imaging under ultra-low illumination conditions. The HS3N integrates the physical forward model of SPI with an untrained neural network regularized by both sparsity priors and Hessian-based structural constraints, enabling effective noise suppression while preserving structural fidelity and continuity. Both simulated and experimental results demonstrate that HS3N enables high-fidelity reconstructions under ultra-low NIR photon levels down to ~0.01 photons per pixel. Furthermore, we demonstrate its dynamic capability by monitoring the dynamic evolution and detachment of infrared-absorbing droplets, at a frame rate of ~20 Hz under ~0.19 photons per pixel, highlighting its potential for high-sensitivity infrared inspection. The proposed reconstruction framework paves the way for practical NIR imaging in extreme low light conditions, which can be extended to visible, mid-infrared or terahertz imaging, offering broad potential for photon-efficient sensing across a wide spectral range.