Three-Dimensional Pulsed-Laser Imaging via Compressed Sensing Reconstruction Based on Proximal Momentum-Gradient Descent

Compressed sensing (CS) is a promising approach to enhancing the spatial resolution of images obtained from few-pixel array sensors in three-dimensional (3D) laser imaging scenarios. However, traditional CS-based methods suffer from insufficient range resolutions and poor reconstruction quality at low CS sampling ratios. To solve the CS reconstruction problem under the time-of-flight (TOF)-based pulsed-laser imaging framework, a CS algorithm based on proximal momentum-gradient descent (PMGD) is proposed in this paper. To improve the accuracy of the range and intensity reconstructed from overlapping samples, the PMGD framework is developed by introducing an extra fidelity term based on a pulse shaping method, in which the reconstructed echo signal obtained from each sensor pixel can be refined during the iterative reconstruction process. Additionally, noise level estimation with the fast Johnson–Lindenstrauss transform is adopted, enabling the integration of a denoising neural network into PMGD to further enhance reconstruction accuracy. The simulation results obtained on real datasets demonstrate that the proposed method can yield more accurate reconstructions and significant improvements over the recently developed CS-based approaches.