Three-Dimensional Image Reconstruction With Enhanced Spatial Accuracy for Pulsed-Laser Imaging Based on Compressive Sensing

Abstract

Compressive sensing (CS) has been intensively studied as a superresolution technique in image acquisition and reconstruction. It has also been introduced into three-dimensional (3-D) laser imaging to enhance the spatial resolution of images obtained from existing few-pixel array sensors. These approaches, however, either suffer from low range resolution or lack long-range detecting abilities. To solve the CS reconstruction problem under the 3-D pulsed-laser imaging framework, we build a sampling model to combine the CS sampling process with the time-of-flight-based range measurement procedure in pulsed-laser imaging. To explore range information from the overlapping echo signals, we develop a gradual alternating minimization algorithm with a designed range equivalent matrix, in which the feasible region of the solution gradually shrinks to make the reconstruction procedure tractable. To further enhance the quality of the reconstructed intensity image, we introduce rank minimization and a fallback mechanism, in which the sparsity and stability of the reconstructed intensity image is refined. In this manner, the accuracy of reconstructed 3-D images can be improved in both the pixel direction and range direction. The simulation results based on real datasets demonstrate that our proposed scheme can yield superior accurate reconstruction and leads to significant improvements compared with recent approaches.