Performance comparison of ghost imaging versus conventional imaging in photon shot noise cases

The performances of ghost imaging and conventional imaging in photon shot noise cases are investigated. We define an imaging signal-to-noise ratio called SNRtranwhere only the object’s transmission region is used to evaluate the imaging quality and it can be applied to ghost imaging(GI) with any random pattern. Both the values SNRtran GIof GI and SNRtran CIof conventional imaging in photon shot noise cases are deduced from a simple statistical analysis. The analytical results, which are backed up by numerical simulations, demonstrate that the value SNRtran GIis related to the ratio between the object’s transmission area Aoand the number density of photons illuminating the object plane Io, which is similar to the theoretical results based on the first principle of GI with a Gaussian speckle field deduced by B. I. Erkmen and J. H. Shapiro [in Adv. Opt. Photonics 2, 405–450(2010)]. In addition, we also show that the value SNRtran CIwill be larger than SNRtran GIwhen Ao is beyond a threshold value.

Smooth Archimedean-spiral ring waveguide for cold atomic gyroscope

We propose a robust scheme that creates a toroidal magnetic potential on a single-layer atom chip. The wire layout consists of two interleaved Archimedean spirals, which avoids the trapping perturbation caused by the input and output ports. By using a rotation bias field, the minimum of the time-averaged orbiting potential is lifted from zero, and then a relatively smooth and harmonic ring trap is formed. The location of the waveguide is immune to the magnetic variations, as it is only determined by the wire layout. The ring waveguide offers an ideal solution to developing a compact and portable atomic Kyroscope.

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.

Deep plug-and-play priors for spectral snapshot compressive imaging

We propose a plug-and-play(Pn P) method that uses deep-learning-based denoisers as regularization priors for spectral snapshot compressive imaging(SCI). Our method is efficient in terms of reconstruction quality and speed trade-off, and flexible enough to be ready to use for different compressive coding mechanisms. We demonstrate the efficiency and flexibility in both simulations and five different spectral SCI systems and show that the proposed deep Pn P prior could achieve state-of-the-art results with a simple plug-in based on the optimization framework. This paves the way for capturing and recovering multi-or hyperspectral information in one snapshot,which might inspire intriguing applications in remote sensing, biomedical science, and material science. Our code is available at: https://github.com/zsm1211/Pn P-CASSI.

A FAST CONVERGING SPARSE RECONSTRUCTION ALGORITHM IN GHOST IMAGING

A fast converging sparse reconstruction algorithm in ghost imaging is presented. It utilizes total variation regularization and its formulation is based on the Karush-Kuhn-Tucker (KKT) theorem in the theory of convex optimization. Tests using experimental data show that, compared with the algorithm of Gradient Projection for Sparse Reconstruction (GPSR), the proposed algorithm yields better results with less computation work.

Influence of the source’s energy fluctuation on computational ghost imaging and effective correction approaches

We investigate the influence of the source’s energy fluctuation on both computational ghost imaging and computational ghost imaging via sparsity constraint,and if the reconstruction quality will decrease with the increase of the source’s energy fluctuation.In order to overcome the problem of image degradation,a correction approach against the source’s energy fluctuation is proposed by recording the source’s fluctuation with a monitor before modulation and correcting the echo signal or the intensity of computed reference light field with the data recorded by the monitor.Both the numerical simulation and experimental results demonstrate that computational ghost imaging via sparsity constraint can be enhanced by correcting the echo signal or the intensity of computed reference light field,while only correcting the echo signal is valid for computational ghost imaging.

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.

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.

Motion de-blurring by second-order intensity-correlated imaging

For a Hanbury Brown and Twiss system, the influence of relative motion between the object and the detection plane on the resolution of second-order intensity-correlated imaging is investigated. The analytical results, which are backed up by experiments, demonstrate that the amplitude and mode of the object＇s motion have no effect on the second-order intensity-correlated imaging and that high-resolution imaging can be always achieved by using a phase-retrieval method from the diffraction patterns. The use of motion de-blurring imaging for this approach is also discussed.