Single-photon 3D imaging with a multi-stage network

Active single-photon 3D imaging technology has been applied to 3D imaging of complex scenes in many frontier fields such as biomedicine, remote sensing mapping, etc. However, single-photon 3D imaging with strong background noise is still a major challenge. Several classical algorithms and machine learning methods have been proposed to solve the problem. In this paper, we propose a novel multi-stage synergistic recovery network to reconstruct an accurate depth map. In the model, we first extract multi-scale feature information using encoder and decoder architectures, then combine them with an original resolution network that retains complete spatial location information. Through this way, we can compensate the deficiencies of the original resolution network for multi-scale local feature extraction. Moreover, a self-supervised attention module (SAM) is constructed to weight local features between different stages, optimizing the feature exchange between different stages of the multi-stage architecture network. Our method currently performs the best of all the tested methods.