Deep-learning based photon-efficient 3D and reflectivity imaging with a 64 × 64 single-photon avalanche detector array.

Abstract

A single photon avalanche diode (SPAD) is a high sensitivity detector that can work under weak echo signal conditions (≤1 photon per pixel). The measured digital signals can be used to invert the range and reflectivity images of the target with photon-efficient imaging reconstruction algorithm. However, the existing photon-efficient imaging reconstruction algorithms are susceptible to noise, which leads to poor quality of the reconstructed range and reflectivity images of target. In this paper, a non-local sparse attention encoder (NLSA-Encoder) neural network is proposed to extract the 3D information to reconstruct both the range and reflectivity images of target. The proposed network model can effectively reduce the influence of noise in feature extraction and maintain the capability of long-range correlation feature extraction. In addition, the network is optimized for reconstruction speed to achieve faster reconstruction without performance degradation, compared with other existing deep learning photon-efficient imaging reconstruction methods. The imaging performance is verified through numerical simulation, near-field indoor and far-field outdoor experiments with a 64 × 64 SPAD array. The experimental results show that the proposed network model can achieve better results in terms of the reconstruction quality of range and reflectivity images, as well as reconstruction speed.