Ghost Imaging Lidar via Sparsity Constraints in Real Atmosphere

We present a series of results acquired at a 2-kilometer distance using our lidar system under several weather conditions, clear, cloudy, light rain, moderately foggy, and night. The experimental results show that ghost imaging lidar via spar-sity constraints can realize imaging in all these weather conditions.

High-resolution pseudo-inverse ghost imaging

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.

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.

Far-field super-resolution ghost imaging with a deep neural network constraint

Ghost imaging(GI)facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing.However,GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image,imposing a practical limit for its applications.Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network.The resulting hybrid neural network does not need to pre-train on any dataset,and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit.Furthermore,the physical model imposes a constraint to the network output,making it effectively interpretable.We experimentally demonstrate the proposed GI technique by imaging a flying drone,and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio.We believe that this study provides a new framework for GI,and paves a way for its practical applications.

Scalar-matrix-structured ghost imaging

The features of the characteristic matrix used in linear intensity correlation reconstruction methods are directly related to the quality of ghost imaging. In order to suppress the noise caused by the off-diagonal elements in the characteristic matrix, we propose a reconstruction method for ghost imaging called scalar-matrix-structured ghost imaging(SMGI). The characteristic matrix is made to approximate a scalar matrix by modifying the measurement matrix. Experimental results show that SMGI improves the peak signal-to-noise ratio of the object reconstruction significantly compared with differential ghost imaging, even in the case of a nonzero two-arm longitudinal difference, which is a promising result for practical applications.

Color ghost imaging via sparsity constraint and non-local self-similarity

We propose a color ghost imaging approach where the object is illuminated by three-color non-orthogonal random patterns. The object’s reflection/transmission information is received by only one single-pixel detector, and both the sparsity constraint and non-local self-similarity of the object are utilized in the image reconstruction process. Numerical simulation results demonstrate that the imaging quality can be obviously enhanced by ghost imaging via sparsity constraint and nonlocal self-similarity(GISCNL), compared with the reconstruction methods where only the object’s sparsity is used. Factors affecting the quality of GISCNL, such as the measurement number and the detection signal-to-noise ratio, are also studied.

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.

Generation of in situ CRISPR-mediated primary and metastatic cancer from monkey liver

Non-human primates(NHPs)represent the most valuable animals for drug discovery.However,the current main challenge remains that the NHP has not yet been used to develop an efficient translational medicine platform simulating human diseases,such as cancer.This study gen erated an in situ gen e-editi ng approach to in duce efficie nt loss-of-function mutations of Pten and p53 genes for rapid modeling primary and metastatic liver tumors using the CRISPR/Cas9 in the adult cyno molgus mon key.Un der ultraso und guida nee,the CRISPR/Cas9 was injected into the cyno molgus mon key liver through the in trahepatic portal vein.The results showed that the ultraso un d-guided CRISPR/Cas9 resulted in in dels of the Pten and p53 genes in seve n out of eight monkeys.The best mutation efficiencies for Pten and p53 were up to 74.71%and 74.68%,respectively.Furthermore,the morbidity of primary and exte nsively metastatic(lung,splee n,lymph nodes)hepatoma in CRISPR-treated mon keys was 87.5%.The ultraso un d-guided CRISPR system could have great potential to successfully pursue the desired target genes,thereby reducing possible side effects associated with hitting no n-specific off-target genes,and sign ifica ntly in creasing more efficie ncy as well as higher specificity of in situ gene editi ng in vivo,which holds promise as a powerful,yet feasible tool,to edit disease genes to build corresp on ding huma n disease models in adult NHPs and to greatly accelerate the discovery of new drugs and save economic costs.