Three-dimensional (3D) remote imaging attracts increasing attentions in capturing a target’s characteristics. Although great progress for 3D remote imaging has been made with methods such as scanning imaging lidar and pulsed floodlight-illumination imaging lidar, either the detection range or application mode are limited by present methods. Ghost imaging via sparsity constraint (GISC), enables the reconstruction of a two-dimensional N-pixel image from much fewer than N measurements. By GISC technique and the depth information of targets captured with time-resolved measurements, we report a 3D GISC lidar system and experimentally show that a 3D scene at about 1.0 km range can be stably reconstructed with global measurements even below the Nyquist limit. Compared with existing 3D optical imaging methods, 3D GISC has the capability of both high efficiency in information extraction and high sensitivity in detection. This approach can be generalized in nonvisible wavebands and applied to other 3D imaging areas.

We demonstrate for the first time (to our knowledge) that an image with high quality can still be obtained in scattering media by applying the second-order correlation of illuminating light field. The effect of multiple scattering on the imaging quality is analyzed both theoretically and experimentally. Potential applications and methods to further improve the imaging quality in scattering media are also discussed.

Ghost imaging (GI) is a method to nonlocally image an object with a single-pixel detector. However, the speckle's transverse size at the object plane limits the system's imaging resolution for conventional GI linear reconstruction algorithm. By combining the sparsity constraint of imaging object with ghost imaging method, we demonstrate experimentally that ghost imaging via sparsity constraint (GISC) can dramatically enhance the imaging resolution even using the random measurements far below the Nyquist limit. The image reconstruction algorithm of GISC is based on compressive sensing. Factors affecting the reconstruction quality of high-resolution GISC, such as the receiving system's numerical aperture and the object's sparse representation basis, are also investigated experimentally. This high-resolution imaging technique will have great applications in the microscopy and remote-sensing areas.

We present a pseudo-inverse ghost imaging (PGI) technique which can dramatically enhance the spatial transverse resolution of pseudo-thermal ghost imaging (GI). In comparison with conventional GI, PGI can break the limitation on the imaging resolution imposed by the speckle’s transverse size on the object plane and also enables the reconstruction of an $N$-pixel image from much less than $N$ measurements. This feature also allows high-resolution imaging of gray-scale objects. Experimental and numerical data assessing the performance of the technique are presented.

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.

The influence of sparsity property of images on ghost imaging via sparsity constraints (GISC) is investigated. We find experimentally that the reconstruction quality is in proportion to the sparse ratio of images. Employing the method of representation transform to obtain different sparsity properties for the same object, the effect of three sparse representation bases on the quality of GISC is also discussed.