Single-photon light detection and ranging (LiDAR) is constrained by the first photon bias effect, which requires the use of low photon flux to avoid signal distortion. This results in longer detection times, and requirements for rapid detection and imaging are not met. In this study, we propose a single-photon array imaging reconstruction algorithm based on photon waveform recovery. By leveraging the principles of high-flux photon signal distortion and incorporating a high-flux photon detection probability model, we effectively mitigate the imaging distortion issues typically encountered in high-flux single-photon LiDAR. Simulations demonstrate the algorithm's capability to extract highly accurate distance and intensity information. Our analysis reveals that even when the signal flux is increased from the traditional 0.05 to 7 photons, the distance error remains <1 cm, while the intensity error remains <0.13. Furthermore, experiments on high-flux array imaging reveal that at a flux level of 3, the average distance error is 0.476 cm and the intensity error is 0.081, achieving a 5-fold improvement in intensity dynamic range. The proposed algorithm eliminates the dependency on low flux for photon imaging, effectively enabling precise imaging with single-photon LiDAR within complex scenes featuring multiple reflectivity levels and targets at various depths.