Ghost imaging lidar via sparsity constraints

By Chengqiang Zhao, Wenlin Gong, Mingliang Chen, Enrong Li, Hui Wang, Wendong Xu, Shensheng Han. 2012

352 citations recorded by the source corpus.

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. The experimental results demonstrate 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.

Subfields: Acoustics and Ultrasonics, Computational Mechanics, Media Technology

View Paper: https://www.semanticscholar.org/paper/8da2e3530d290d49637e3c864eb4c30c31eae362

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Acoustics and Ultrasonics

1. Hao Wang
2. Guohai Situ
3. Wenlin Gong
4. Shensheng Han
5. Guowei Li
6. Mingliang Chen
7. Meng Lyu

In Computational Mechanics

1. Chenglong Wang
2. Fei Wang
3. Yu Zhang
4. Guohai Situ
5. Wenlin Gong
6. Shensheng Han
7. Mingliang Chen

In Media Technology

1. Guohai Situ
2. Mingliang Chen

Citations in the corpus

• Deep-learning-based ghost imaging (2017, background): “One popular solution to decrease the acquisition time is to combine GI and compressive sensing (CS) theory 10,16,18 .”
• Far-field super-resolution ghost imaging with a deep neural network constraint (2022, methodology): “In many applications such as remote sensing 10 , a rotating ground glass (RGG) is frequently used to generate speckle illumination patterns compared with other programmable modulation strategies, e.g., digital micromirror device 11 owing to its high power endurance and cost ef fi ciency.”