Ghost Imaging Method Research Articles

Two-dimensional correlation analysis for x-ray photoelectron spectroscopy

X-ray photoelectron spectroscopy (XPS) measures the binding energy of core-level electrons, which are well-localised to specific atomic sites in a molecular system, providing valuable information on the local chemical environment. The technique relies on measuring the photoelectron spectrum upon x-ray photoionisation, and the resolution is often limited by the bandwidth of the ionising x-ray pulse. This is particularly problematic for time-resolved XPS, where the desired time resolution enforces a fundamental lower limit on the bandwidth of the x-ray source. In this work, we report a novel correlation analysis which exploits the correlation between the x-ray and photoelectron spectra to improve the resolution of XPS measurements. We show that with this correlation-based spectral-domain ghost imaging method we can achieve sub-bandwidth resolution in XPS measurements. This analysis method enables XPS for sources with large bandwidth or spectral jitter, previously considered unfeasible for XPS measurements.

Computational ghost imaging based on an untrained neural network

Ghost imaging based on deep learning (DLGI) usually employs a supervised learning strategy, and needs a large set of labeled data to train a neural network. However, in many practical applications, it is difficult to obtain sufficient numbers of labeled data for training and the training process often takes a long time. Thus, a computational ghost imaging method based on deep learning using an untrained neural network (UNNCGI) is proposed. The input to the network is just a set of one-dimensional light intensity values collected by a single-pixel detector and the neural network can be automatically optimized to generate restored images through the interaction between the network and the process of computational ghost imaging. Both simulation and experiment confirm the feasibility of this untrained network. The reconstructed image of UNNCGI has good quality, even at low sampling ratios, which improves the imaging efficiency and will promote the practical applications of ghost imaging.

Preconditioned deconvolution method for high-resolution ghost imaging

Ghost imaging (GI) can nonlocally image objects by exploiting the fluctuation characteristics of light fields, where the spatial resolution is determined by the normalized second-order correlation function g ( 2 ) . However, the spatial shift-invariant property of g ( 2 ) is distorted when the number of samples is limited, which hinders the deconvolution methods from improving the spatial resolution of GI. In this paper, based on prior imaging systems, we propose a preconditioned deconvolution method to improve the imaging resolution of GI by refining the mutual coherence of a sampling matrix in GI. Our theoretical analysis shows that the preconditioned deconvolution method actually extends the deconvolution technique to GI and regresses into the classical deconvolution technique for the conventional imaging system. The imaging resolution of GI after preconditioning is restricted to the detection noise. Both simulation and experimental results show that the spatial resolution of the reconstructed image is obviously enhanced by using the preconditioned deconvolution method. In the experiment, 1.4-fold resolution enhancement over Rayleigh criterion is achieved via the preconditioned deconvolution. Our results extend the deconvolution technique that is only applicable to spatial shift-invariant imaging systems to all linear imaging systems, and will promote their applications in biological imaging and remote sensing for high-resolution imaging demands.

An optical image encryption based on computational ghost imaging with sparse reconstruction

In the traditional optical cryptosystems based on computational ghost imaging, a large number of phase-only masks or speckle patterns are generated to reconstruct the object image with high visual quality. At the same time, this kind of massive data, which is usually considered as the security keys to enhance the security of the cryptosystem, needs to be stored and transmitted and leads to low efficiency in practical applications. To address this issue, a computational ghost imaging method based on sparse reconstruction with the Barzilai-Borwein gradient projection is proposed from a new perspective of applying the compressive sensing technique in this paper. The measured intensities of the object image are collected by the means of traditional computational ghost imaging, while the security key composed of speckle patterns is compressed using compressive sensing and then transmitted in the secure channel in the proposed method. Consequently, the burden of key management such as storage and transmission can be considerably decreased. Moreover, when a significantly small part of the security key is shared in the secure channel, original information still can be authenticated without clear visualization by calculating the nonlinear correlation map between the object image and its reconstruction. The optical experiments illustrate the feasibility and effectiveness of the proposed method and demonstrate that it can provide an effective alternative for enriching the related research on computational ghost imaging techniques.

Hybrid neural network-based adaptive computational ghost imaging

We propose a hybrid neural network-based adaptive computational ghost imaging (CGI) method to restore clear images of objects with different sub-Nyquist sampling ratios (SNSRs). We design an adaptive method and a hybrid neural network for CGI-based image reconstruction. We use an interference-adding layer in the network of the proposed method (HA) to remove multiple degradations and noise during the training process. We train the network once with simulated data, and it can recover high-quality images from the experimental data at different SNSRs. The effectiveness and advantages of HA are numerically and experimentally studied. HA is helpful for improving the image quality and reconstruction efficiency of CGI.

Compressive Ghost Imaging of the Moving Object Using the Low-Order Moments

Ghost imaging reconstructs the image based on the second-order correlation of the repeatedly measured light fields. When the observed object is moving, the consecutive sampling procedure leads to a motion blur in the reconstructed images. To overcome this defect, we propose a novel method of ghost imaging to obtain the motion information of moving object with a small number of measurements, in which the object could be regarded as relatively static. Our method exploits the idea of compressive sensing for a superior image reconstruction, combining with the low-order moments of the images to directly extract the motion information, which has the advantage of saving time and computation. With the gradual motion estimation and compensation during the imaging process, the experimental results show the proposed method could effectively overcome the motion blur, also possessing the advantage of reducing the necessary measurement number for each motion estimation and improving the reconstructed image quality.

Can the ghost imaging increase the lateral resolution of surface plasmon resonance microscopy?

Surface plasmon resonance (SPR) microscopy is one of the most sensitive optical label-free methods of microscopy. Nevertheless, it does not have a sufficiently high lateral resolution in comparison with other methods of optical microscopy. By analogy with the scattering medium, surface plasmon polaritons (SPPs) blur the observed area. To eliminate this disadvantage, we propose to adapt the method known as ghost imaging (GI), which is notable for its tolerance to environmental aberrations between an object and a camera. In this article, we propose a ghost imaging scheme for a surface plasmon resonance microscope using a pseudo-thermal radiation source. We make a fundamental analysis of the factors affecting the resolution capability of the ghost SPR microscopy. We claim that applying the ghost imaging method to SPR microscopy can improve its lateral resolution by eliminating uncorrelated with modulated radiation phase noise generated by the process of random re-emission of surface electromagnetic wave (SEW) from the site of excitation. In combination of factors, the ghost imaging method of SPR microscopy is potentially capable of becoming outstanding among other methods of microscopy of thin films.

Deep-learning denoising computational ghost imaging

We propose a deep learning denoising computational ghost imaging (CGI) method to obtain a clear object image with a sub-Nyquist sampling ratio. We develop an end-to-end deep neural network (DDANet) for CGI image reconstruction. DDANet uses a one-dimensional (1-D) bucket signals (BSs) and multiple tunable noise-level maps as input, and outputs a clear image. We train DDANet with simulated BSs and ground-truth pairs, and then retrieve the object image directly from an experimental obtained 1-D BSs. The effectiveness of the proposed method is experimentally investigated. The proposed method has practical applications in image denoising and enhancement of the CGI and single-pixel computational imaging.

Ghost polarimetry using Stokes correlations

We present here a novel ghost polarimeter based on Stokes parameter correlations and a spatially incoherent classical source with adjustable polarization state and Gaussian statistics. The setup enables extracting the four amplitudes and three phase differences related to the spectral $ 2 \times 2 $2×2 complex Jones matrix of any transmissive polarization-sensitive object. Our work extends the ghost imaging methods from the traditional intensity correlation measurements to the detection of polarization state correlations.

基于卷积神经网络的计算鬼成像方法研究

基于卷积神经网络的计算鬼成像方法研究

基于Tikhonov正则化的计算鬼成像方法

基于Tikhonov正则化的计算鬼成像方法

An Effective Computational Ghost Imaging Based on Noise Estimation and Elimination

To improve the quality of ghost image, we propose an efficient computational ghost imaging method in this article. The primary idea is to estimate the noise value of the ghost image by analyzing and eliminating the source of the noise. The innovativeness of this work lies in analyzing a new means of noise by dissecting the qualitative relationship between transmittance in different objects and speckle patterns. While using a scale factor to describe the change of transmittance at different points of the object. The simulation and experimental results prove the effectiveness and feasibility of the proposed method through two parallel experiments. Compared to other methods, the peak signal-to-noise ratio and contrasts both have significantly increased.

基于区域分割的压缩计算鬼成像方法

基于区域分割的压缩计算鬼成像方法

Pseudo-thermal imaging by using sequential-deviations for real-time image reconstruction.

Ghost imaging technologies acquire images through intensity correlation of reference patterns and bucket values. Among them, an interesting method named correspondence imaging can generate positive-negative images by only conditionally averaging reference patterns, but still requires full/over sampling to treat the ensemble average of bucket values as a selection criteria, causing a long acquisition time. Here, we propose a sequential-deviation ghost imaging approach, which can realize real-time reconstructions of positive-negative images with a high image quality close to that of differential ghost imaging. Since it is no longer necessary to compare with the ensemble average, this method can improve the real-time performance. An explanation of its essence is also given here. Both simulation and experimental results have demonstrated the feasibility of this technique. This work may complement the theory of ghost imaging.

Generation and characterization of position-momentum entangled photon pairs in a hot atomic gas cell.

Continuous-variable position-momentum entanglement (or Einstein-Podolsky-Rosen entanglement) of two particles has played important roles in the fundamental study of quantum physics as well as in the progress of quantum information. In this paper, we propose a scheme to generate Einstein-Podolsky-Rosen (EPR) position-momentum entangled photon pairs efficiently via spontaneous four-wave mixing (SFWM) process in a hot rubidium gas cell. The EPR entanglement between the photon pair is measured and characterized by using the ghost interference and the ghost imaging method. Due to the simplicity of the experimental setup and the high photon pair generation rate, our EPR entangled photon source may has potential applications in quantum imaging, hyperentanglement preparation and atomic ensemble based quantum information processing and quantum communication protocols.

High compressive ghost imaging method based on discrete cosine transform using weight coefficient matching

ABSTRACTGhost imaging can capture a scene without directly catching sight of the target, but in the case of high compression ratio, high-quality imaging is challenging at present. Here a ghost imaging method using weight coefficient matching based on discrete cosine transform (DCT) is proposed, in which the high-quality target images can be retrieved by obtaining the larger weight value in one-dimensional (1D) DCT spectrum. In the case of low sampling, the proposed method can not only acquire the spectral coefficients with large weight, but also put them in the correct position; eventually it can obtain the desired image by inverse discrete cosine transform of the spectrum. At the same sampling ratio, both simulation results and optical experiments show that the reconstructed image quality of the proposed method exhibits better performance. In addition, even the sampling ratio is as low as about 3%, the outline of the target image can still be roughly recognized.

Edge detection based on joint iteration ghost imaging

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system requires large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of projected Landweber iteration regularization and guided filter ghost imaging method has been proposed, which can improve the feature detection quality in ghost imaging. This method can also achieve high-quality imaging. Simulation and experiment results show that the spatial information and edge information of target object are successfully recovered from the random speckle patterns without special coding under a low measurement times, and the edge image quality is improved remarkably. This approach improves the the applicability of ghost imaging and can satisfy the practical application fields of imaging and edge detection at the same time.

Grayscale and color ghost-imaging of moving objects by memory-enabled, memoryless and compressive sensing algorithms

Grayscale and color computational ghost imaging of a moving object is investigated. Three different ghost imaging algorithms, memory-enabled, memoryless and compressive sensing, are employed and compared. A speckle resizing method is also applied to enhance the features of the obtained ghost images. The results indicate that the compressive sensing algorithm provides better SNR and visibility values, while the memory-enabled algorithm offers higher imaging speeds. It is shown that for shot numbers ranging from 150–400, SNR values as high as 1.5, visibilities around 0.95 and maximum target speeds about 0.65 pixel/s are affordable by proper selection of the applied ghost imaging algorithm. Also, a method for color ghost imaging of moving objects through RGB illumination is introduced. Furthermore, results for experimental computational ghost imaging of a moving object along with the maximum practicable speed are included.

Noise reduction from two frame speckle-shifting ghost images with morphology algorithms

ABSTRACTEdge detection is the basis of image segmentation and object recognition, as edge generally contains important information of an object. In this paper, we propose a novel speckle-shifting ghost imaging (SSGI) method to extract the edge of an unknown object. In this method, the gradient operation is directly carried out to the illumination patterns rather than the captured object image. The structured patterns for illumination are only divided into two groups, which can extract the edge in all directions. The imaging result is clearer than the conventional SSGI, but the noise is still serious. To solve the problem, we further investigate a denoising method with morphology algorithms, such as frame difference and connected region labelling. Numerical simulations and experiments are carried out to verify the feasibility and effectiveness.

Turbulence-free intensity fluctuation self-correlation imaging with sunlight

Since its first experimental demonstration in 1995, ghost imaging has attracted a great deal of attention due to its potential applications. In Yanhua Shih’s paper (Meyers R E, Deacon K S, Shih Y H 2011 <i>Appl. Phys. Lett.</i> <b>98</b> 111115; Meyers R E, Deacon K S, Shih Y H 2012 <i>Appl. Phys. Lett.</i> <b>100</b> 131114), he pointed out that " one of the useful features is the turbulence insensitivity of thermal light ghost imaging, i.e., atmospheric turbulence would not have any influence on the ghost images of sunlight”. However, in Jeffrey H. Shapiro’s view (Shapiro J H 2012 arXiv:1201.4513 [quant-ph]), lens-less pseudo-thermal ghost imaging is not immune to spatial resolution loss from the presence of atmospheric turbulence along the propagation paths, unless the source diameter is less than the source-plane turbulence coherence length. In the present paper, we find that the second order self-correlation of sunlight intensity is the case that can be satisfied with both theories of Shih and Shapiro. In this paper, the second order self-correlation of sunlight intensity (the intensity variance of the images acquired by the CCD camera), rather than the total intensity correlation between images and bucket detector signals (the traditional ghost imaging method), is calculated to recover the high-quality images in turbulent atmosphere under a few hundred measurements. Inspired by Boyd’s paper, titled " thermal ghost imaging with averaged speckle patterns” (Zerom P, Shi Z, O’Sullivan M N, Chan K W C, Krogstad M, Shapiro J H, Boyd R W 2012 <i>Phys. Rev. A</i> <b>86</b> 063817), turbulence insensitivity of sunlight ghost imaging is demonstrated both theoretically and experimentally in this article. It is found that thermal ghost imaging system whose coherence time need not to be controlled to match the speed of the detectors, ghost imaging with sunlight, which has always been considered intriguing and highly desirable, now is realizable. We present theoretical and experimental results showing that a sunlight self-correlation ghost imaging system can produce high-quality images even when it uses an slow detector and passes through the turbulence near ground, as long as the signal variation is predominantly caused by the fluctuation of the sunlight intensity rather than other noise sources. Our scheme can also be used to improve the image quality in other wave bands such as infrared and ultraviolet, in the case where an poor image quality results from the turbulence or other random disturbances on the wavefront.