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.

Optical imaging has gained widespread use in biomedical imaging because of its high resolution and capacity to provide diverse information. However, the propagation of light in biological tissues is diffused by scattering, resulting in limited penetration depth and poor resolution. Achieving super-resolution imaging through/in scattering media is highly desirable but extremely challenging. Various fluorescence microscopies have achieved resolutions down to nanometers using sparsely excited fluorescent particles and their corresponding algorithms. However, such techniques are limited to discrete cells in vitro rather than in vivo. Understanding the functions and mechanisms of cells in their microenvironment is critical. Therefore, there is an urgent need for in-situ super-resolution imaging.

Infrared dim target detection and tracking have been applied across diverse fields, including intelligent driving and military technology. However, traditional detection and tracking methods rely primarily on target intensity information, making it difficult to fully utilize high-dimensional optical field information such as spatial and spectral data, which ultimately limits further performance enhancements. Spectral imaging, which simultaneously captures both spatial and spectral information of targets, suffers from a reduced detection signal-to-noise ratio (SNR), which hinders the detection of weak and small targets. We propose an infrared dim target detection and tracking method for a snapshot spectral ghost imaging camera. The method leverages the correlation characteristics of spatial intensity fluctuations corresponding to encoded targets to enhance the detection performance for weak and small targets. Furthermore, by employing three-dimensional matched filtering and particle filtering, it exploits prior spectral and spatial information to improve both the detection and tracking performance. Simulations and experiments verified that this method can track the spectrum and trajectory of a target when the detection SNR is as low as 3, and the average error of the tracking trajectory is less than 1 pixel using the continuity prior information of the spectrum and space in the time dimension.

Point spread function (PSF) engineering is a promising approach for passive, snapshot 3D imaging with a single detector. A widely used technique is the double-helix PSF (DH-PSF), which employs a specialized phase mask at the pupil plane to modulate incident light, generating rotationally varying PSFs with defocus. By leveraging a precalibrated depth-dependent PSF model, the depth information of the target surface can be recovered from a snapshot measurement. However, existing reconstruction algorithms often lack efficiency and accuracy, primarily due to the block-wise processing of conventional methods or the failure to incorporate physical priors in end-to-end neural networks. To address these limitations, we propose a physics-guided deep unfolding network (PG-DUN) for snapshot 3D imaging with DH-PSFs. By explicitly embedding the imaging model into the deep neural network, our DUN can naturally reconstruct the 2D image and depth map simultaneously, contributing to more accurate and efficient reconstruction than previous approaches. The feasibility and effectiveness of the proposed method are validated through extensive experiments on simulated and real-world data. The proposed method can serve as a prototype for a deep learning-based reconstruction model in similar deconvolution tasks. Its key innovation—an accelerated deconvolutional gradient descent design—functions as a plug-and-play component that enhances the reconstruction accuracy of any deep neural network with negligible added computational cost.