Performance analysis of ghost imaging lidar in background light environment

The effect of background light on the imaging quality of three typical ghost imaging(GI) lidar systems(namely narrow pulsed GI lidar, heterodyne GI lidar, and pulse-compression GI lidar via coherent detection) is investigated. By computing the signal-to-noise ratio(SNR) of fluctuation-correlation GI, our analytical results, which are backed up by numerical simulations, demonstrate that pulse-compression GI lidar via coherent detection has the strongest capacity against background light, whereas the reconstruction quality of narrow pulsed GI lidar is the most vulnerable to background light. The relationship between the peak SNR of the reconstruction image andσ(namely, the signal power to background power ratio) for the three GI lidar systems is also presented, and theresults accord with the curve of SNR-σ.

Ghost imaging for an axially moving target with an unknown constant speed

The influence of the axial relative motion between the target and the source on ghost imaging(GI) is investigated.Both the analytical and experimental results show that the transverse resolution of GI is reduced as the deviation of the target’s center position from the optical axis or the axial motion range increases. To overcome the motion blur,we propose a deblurring method based on speckle-resizing and speed retrieval, and we experimentally validate its effectiveness for an axially moving target with an unknown constant speed. The results demonstrated here will be very useful to forward-looking GI remote sensing.

Single-pixel imaging using physics enhanced deep learning

Single-pixel imaging(SPI) is a typical computational imaging modality that allows two-and three-dimensional image reconstruction from a one-dimensional bucket signal acquired under structured illumination.It is in particular of interest for imaging under low light conditions and in spectral regions where good cameras are unavailable.However,the resolution of the reconstructed image in SPI is strongly dependent on the number of measurements in the temporal domain.Data-driven deep learning has been proposed for high-quality image reconstruction from a undersampled bucket signal.But the generalization issue prohibits its practical application.Here we propose a physics-enhanced deep learning approach for SPI.By blending a physics-informed layer and a model-driven fine-tuning process,we show that the proposed approach is generalizable for image reconstruction.We implement the proposed method in an in-house SPI system and an outdoor single-pixel LiDAR system,and demonstrate that it outperforms some other widespread SPI algorithms in terms of both robustness and fidelity.The proposed method establishes a bridge between data-driven and model-driven algorithms,allowing one to impose both data and physics priors for inverse problem solvers in computational imaging,ranging from remote sensing to microscopy.

Influence of the sparsity of random speckle illumination on ghost imaging in a noise environment

The influence of the sparsity of random speckle illumination on traditional ghost imaging(GI) and GI via sparsity constraint(GISC) in a noise environment is investigated. The experiments demonstrate that both GI and GISC obtain their best imaging quality when the sparsity of random speckle illumination is 0.5, which is also explained by some parameters such as detection of the signal to noise ratio and mutual coherence of the measurement matrix.