Source-linked AI summary
Ghost imaging lidar via sparsity constraints
Chengqiang Zhao, Wenlin Gong, Mingliang Chen, Enrong Li, Hui Wang, Wendong Xu, Shensheng Han
TL;DR
Remote-sensing lidar must balance scanning speed and detection efficiency with high-resolution imaging. This paper proposes GISC lidar using ghost imaging and sparsity constraints, experimentally reconstructing targets about 900 m away with 20 mm resolution.
Problem
Existing scanning lidar is difficult to use for high-speed imaging, while non-scanning lidar divides reflected light among many pixels; remote ghost-imaging lidar therefore needs practical high-resolution demonstrations.
Method
The system combines pseudo-thermal ghost imaging with sparsity-constrained compressed-sensing reconstruction, representing the target in a sparse basis from CCD and PMT measurements.
Results
20 mm resolution was clearly differentiated for targets located about 900 m away, demonstrating super-resolution imaging with GISC lidar.
Takeaways & Limitations
GISC lidar combines reported long detection distance, high imaging speed, and super-resolution capabilities without scanning the target.
Abstract
from arXiv · showhide
For remote sensing, high-resolution imaging techniques are helpful to catch more characteristic information of the target. We extend pseudo-thermal light ghost imaging to the area of remote imaging and propose a ghost imaging lidar system. For the first time, we demonstrate experimentally that the real-space image of a target at about 1.0 km range with 20 mm resolution is achieved by ghost imaging via sparsity constraints (GISC) technique. The characters of GISC technique compared to the existing lidar systems are also discussed.