Single-pixel imaging (SPI) is a typical computational imaging modality that allows two- and three-dimensional image reconstruction from a one-dimensional bucket signal acquired under structured illumination. It is in particular of interest for imaging under low light conditions and in spectral regions where good cameras are unavailable. However, the resolution of the reconstructed image in SPI is strongly dependent on the number of measurements in the temporal domain. Data-driven deep learning has been proposed for high-quality image reconstruction from a undersampled bucket signal. But the generalization issue prohibits its practical application. Here we propose a physics-enhanced deep learning approach for SPI. By blending a physics-informed layer and a model-driven fine-tuning process, we show that the proposed approach is generalizable for image reconstruction. We implement the proposed method in an in-house SPI system and an outdoor single-pixel LiDAR system, and demonstrate that it outperforms some other widespread SPI algorithms in terms of both robustness and fidelity. The proposed method establishes a bridge between data-driven and model-driven algorithms, allowing one to impose both data and physics priors for inverse problem solvers in computational imaging, ranging from remote sensing to microscopy.

Three-dimensional ghost imaging LiDAR via sparsity constraint (3D GISC LiDAR), as a staring imaging method, can obtain both the range information and spatial distribution of a remote target with a single-pixel time-resolved detector. However, previous demonstrations mainly focused on the relatively static scene in visible light. Here we propose an airborne near infrared 3D GISC LiDAR system and airborne high-resolution imaging is implemented. Experimental results show that an image with 0.48 m horizontal resolution as well as 0.5 m range resolution at approximately 1.04 km height can be achieved. Some considerations on the improvement of this LiDAR system are also discussed.

An iterative pseudo-inverse ghost imaging (IPGI) method is proposed based on iterative denoising and pseudo-inverse ghost imaging (PGI). The background noise in the imaging is eliminated in iterations by setting an appropriate threshold. The IPGI method provides a significantly larger enhancement of the peak-signal-to-noise ratio (PSNR) than the PGI technique for binary objects. Experiments and data analyses are performed to evaluate the performance of the proposed method. Compared with conventional GI, differential GI (DGI), and PGI methods, the proposed method has the highest performance in visual effects and significantly improves the imaging quality. For a certain PSNR, the proposed method provides satisfactory performance in terms of computing time.

In this paper, a tracking and pointing control system with dual-FSM (fast steering mirror) composite axis is proposed. It is applied to the target-tracking accuracy control in a 3D GISC LiDAR (three-dimensional ghost imaging LiDAR via sparsity constraint) system. The tracking and pointing imaging control system of the dual-FSM 3D GISC LiDAR proposed in this paper is a staring imaging method with multiple measurements, which mainly solves the problem of high-resolution remote-sensing imaging of high-speed moving targets when the technology is transformed into practical applications. In the research of this control system, firstly, we propose a method that combines motion decoupling and sensor decoupling to solve the mechanical coupling problem caused by the noncoaxial sensor installation of the FSM. Secondly, we suppress the inherent mechanical resonance of the FSM in the control system. Thirdly, we propose the optical path design of a dual-FSM 3D GISC LiDAR tracking imaging system to solve the problem of receiving aperture constraint. Finally, after sufficient experimental verification, our method is shown to successfully reduce the coupling from 7% to 0.6%, and the precision tracking bandwidth reaches 300 Hz. Moreover, when the distance between the GISC system and the target is 2.74 km and the target flight speed is 7 m/s, the tracking accuracy of the system is improved from 15.7 μrad (σ) to 2.2 μrad (σ), and at the same time, the system recognizes the target contour clearly. Our research is valuable to put the GISC technology into practical applications.

Noise term in the reconstruction matrix in ghost imaging is a major cause of blurring imaging results. To remedy this problem, we propose a new ghost imaging method based on the binomial theorem to reduce the level of noise. In our method, images with low-level noise can be generated by constructing a binomial formula using high-order imaging results that are acquired by reintroducing the reconstruction result back into the imaging formula repeatedly. Experimental and simulation results demonstrate that our method is effective in improving imaging quality and anti-interference performance and reducing computing time, making it useful for practical applications.