First-Photon Imaging Using a Data Preprocessing Method Based on Multiscale Image Segmentation

First-photon imaging is a photon-efficient computational imaging technique that reconstructs an image by recording only the first-photon arrival event at each spatial location and then optimizing the recorded photon information. This computational imaging method maximizes the advantages of less-photon imaging, but in practice, it is hard to obtain high-quality reconstructed images due to the extremely low signal-to-noise ratio (SNR). To address this problem, we propose a data processing method to remove the noise and improve the accuracy of first-photon signal selection. Using this method, we conducted a 10 km first-photon imaging experiment in an urban environment and reduced the root-mean-square error (RMSE) value of the first photon 3-D reconstructed image by more than 50% compared with the conventional data processing method. We believe that this method offers a novel approach for accurately extracting signal photons under extremely weak detection conditions.