Dynamic point target detection is vital in fields such as computer vision, remote sensing, and the military. With the technical development, there is an increasing demand for real-time and highly sensitive target detection, in which single-photon imaging has great potential and application significance. Unfortunately, most currently available single-photon detectors have only single-pixel or limited resolution, and traditional scanning imaging with these detectors will cause time waste. Therefore, single-pixel single-photon imaging based on compressed sensing has become a research hotspot. However, traditional single-photon detection relies on photon number accumulation, which requires increased time to resist shot noise interference under the extremely low target signal, thus reducing detection speed. In recent years, first-photon imaging technology has been proposed to achieve imaging by employing only one photon per pixel based on utilizing time information of the photon, but until now this technology can only be applied to active lidar systems, limiting its application scenarios. Thus, we propose a passive compressed sensing single-photon imaging method for weak target detection, which utilizes first-photon time information to improve the sensitivity and sampling speed of point target detection. Simulation analysis and experimental verification show that this method is feasible for high-precision imaging and positioning of weak targets in passive detection conditions and suitable for the simultaneous detection of multiple moving point targets. Our study is of great significance for improving the performance of weak target detection technology.

Firstly, we analyze the statistical relationship between the first-photon time and average photon number under the influence of shot noise in single-photon detection. The results show that as the average photon number increases, the probability of a smaller first-photon time increases (Fig. 1). Based on this, a point target detection method based on compressed sensing imaging with first-photon time measurement is proposed. This method employs a digital micromirror device (DMD) to spatially modulate a target with photon level and measures the arrival time of the first photon on the single-photon detector after each modulation (Fig. 2). By setting a threshold, the corresponding relationship between the target position and the modulation matrix is estimated using the first-photon time, leading to a binary measurement result of 0 or 1. Then, the target-related information can be extracted from the single photon detected after each modulation. By adopting the estimation results and modulation matrices, the point target image is reconstructed via a compressed sensing algorithm to achieve target position detection. Finally, a denoising algorithm based on frame difference is proposed to calculate the intensity difference between the neighbor pixels in adjacent frames and thus identify a reconstructed point as a target or noise point with a set threshold. As a result, the reconstructed noise can be removed from dynamic detection results, with information on moving point targets retained (Fig. 3).

Different from traditional compressive single-photon imaging based on photon number accumulation, this method leverages first-photon time information in the passive detection mode. Within each modulation, only one photon is needed to be detected. By extracting helpful information from the first-photon time and combining it with the compressed sensing algorithm, we can conduct imaging on point targets quickly and accurately and locate them with a very low sampling number and extremely low photon numbers. We first verify the effectiveness of this method by simulations. The effects of time threshold, measurement matrix sparsity, and modulation time on detection performance are studied (Fig. 4, Tables 1-3). It is proved that with optimal parameters, the point target detection probability can be higher than 99%. Then, an optical system is built for verifying the performance in real experiments. The experimental results show that for a 64 pixel×64 pixel resolution image, point targets can be accurately detected with only a 2.2% sampling rate. In multi-frame detection results of a moving target, the frame-difference denoising algorithm can remove noise points from the reconstructed results and provide the trajectory of moving point targets (Fig. 7). Furthermore, this method is also applicable to the simultaneous detection of multiple moving point targets (Fig. 8).

We propose a point target detection method based on compressive single-photon imaging with first-photon time measurement. This method breaks through the limitation of only employing single-dimensional information of photon number in passive detection by utilizing first-photon arrival time for target detection. For each modulation, at most one photon is required, which dramatically improves the utilization efficiency of photon information and achieves high-accurate point target detection in an ultra-weak environment. Additionally, compared with the traditional scanning single-photon imaging method, we adopt the compressed sensing algorithm to achieve sparse point target reconstruction and position detection at a low sampling rate. Finally, for the dynamic target detection results, we propose an adjacent frame difference algorithm that can reduce the reconstructed noise and realize high-quality detection of multiple moving point targets simultaneously. Simulations show that the probability of point target detection can be higher than 99%, and optical experiments prove that a point target can be accurately detected under a sampling rate of only 2.2%, which demonstrates the feasibility of this method in real conditions.