Three-dimensional ghost imaging LiDAR via sparsity constraint (3D GISC LiDAR), as a staring imaging method, can obtain both the range information and spatial distribution of a remote target with a single-pixel time-resolved detector. However, previous demonstrations mainly focused on the relatively static scene in visible light. Here we propose an airborne near infrared 3D GISC LiDAR system and airborne high-resolution imaging is implemented. Experimental results show that an image with 0.48 m horizontal resolution as well as 0.5 m range resolution at approximately 1.04 km height can be achieved. Some considerations on the improvement of this LiDAR system are also discussed.

Imaging into thick scattering medium is a long-standing challenge. Beyond the quasi-ballistic regime, multiple scattering scrambles the spatiotemporal information of incident/emitted light, making canonical imaging based on light focusing nearly impossible. Diffusion optical tomography (DOT) is one of the most popular approach to look inside scattering medium, but quantitatively inverting the diffusion equation is ill-posed, and prior information of the medium is typically necessary, which is nontrivial to obtain. Here, we show theoretically and experimentally that, by synergizing the one-way light scattering characteristic of single pixel imaging with ultrasensitive single photon detection and a metric-guided image reconstruction, single photon single pixel imaging can serve as a simple and powerful alternative to DOT for imaging into thick scattering medium without prior knowledge or inverting the diffusion equation. We demonstrated an image resolution of 12 mm inside a 60 mm thick (∼ 78 mean free paths) scattering medium.

We have constructed a chirped amplitude modulation heterodyne ghost imaging (CAM-HGI) experimental system that demonstrates a robust ability against background light in experiments. In the experiments, the background light is simulated by irradiating a spatiotemporal random modulated light field onto the target. The effects of background light, modulation depth and modulation duration of the signal light source on CAM-HGI are investigated experimentally. The results show that the quality of CAM-HGI can be improved by increasing the modulation depth and the modulation duration of the signal light source, and more importantly, an image with a good signal-to-noise ratio (SNR) can be achieved even when the irradiation SNR is lower than −30 dB. This technique of CAM-HGI has an important application prospect for laser imaging in strong background light environments.

Vibration, rotation and movement of the object generally have adverse effects on the detection and imaging for active and passive sensing. However, by combination of coherent detection and ghost imaging (GI), we propose a novel lidar system called coherent detection ghost imaging via sparsity constraints (GISC) lidar. The phase information of the signal light is detected by coherent detection. By analysing the phase change of the signal light, the vibrating information of the object is obtained. Backed up by experiments, coherent detection GISC lidar is demonstrated to image vibrating objects. Based on the short-time Fourier transform, two objects vibrating with different phases and amplitudes are experimentally demonstrated to image independently by coherent detection GISC lidar. Through the vibrating information, the capability of object identification can even be proposed. We strongly believe that coherent detection GISC lidar will pave the way for vibrating objects imaging and identification.

Aberration correction is critical for obtaining sharp images but remains a challenging task. Owing to its ability to record both spatial and angular information of light rays, light field imaging is a powerful method to measure and correct optical aberrations. However, current methods need extensive calibrations to obtain prior information about the camera, which is restrictive in real-world applications. In this work, we propose a two-stage blind aberration correction method for light field imaging, which leverages self-supervised learning for general blind aberration correction and low-rank approximation to exploit the specific correlations of light fields to further abate aberrations. We demonstrated experimentally the superiority of our method over current state-of-the-art.

The rail transit Codes contain a large number of schemaless data and text, which are difficult to extract and process. Knowledge Graph can handle large-scale schemaless data, thus it is used to effectively store and manipulate relevant information of rail transit Codes. As an essential component, Chinese word segmentation is a difficult problem for building the Knowledge Graph of rail transit Codes. In this paper, we propose a bi-directional maximum matching algorithm and some rules based Chinese word segmentation method, for design Codes of rail transit. In the proposed method, the item information, attribute values and Code names are first segmented by preprocessing. Then, the bidirectional matching algorithm with double dictionary is used to generate the preliminary results. Finally, the preliminary results are further optimized with rules to generate the final results. With testing some design Codes, the experimental results show that the proposed method can improve the accuracy and efficiency of Chinese word segmentation in this field, and it can deal with the update of noun entity library.