Differential Ghost Imaging Research Articles

Computational underwater ghost imaging based on scattering-and-absorption degradation.

In underwater computational ghost imaging, the presence of scattering and absorption introduces significant degradation, leading to blurring and distortion of illuminating patterns. This work proposes an anti-degradation underwater computational ghost imaging (AUGI) method based on the physical degradation model of underwater forward degradation caused by scattering and absorption. Through AUGI, we can enhance the quality of a reconstructed image by about 10% compared to differential ghost imaging (DGI) as measured by peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as a result of simulations. We experimentally demonstrate the superior effectiveness of this method in the artificial submarine environment. Additionally, benefitting from its simplicity, this method is expected to be applied across a wide range of underwater ghost imaging applications.

Differential ghost imaging with learned modulation patterns

Differential ghost imaging with learned modulation patterns

On the use of superthermal light for imaging applications

Ghost imaging and differential ghost imaging are well-known imaging techniques based on the use of both classical and quantum correlated states of light. The existence of correlations within bipartite states has been shown to be the main resource to implement ghost imaging and differential ghost imaging protocols. Here we analyze the advantages and disadvantages of using two different kinds of superthermal states of light, which are more fluctuating than the typically employed thermal states, and thus originate correlations when divided at a beam splitter. To make a fair comparison, we calculate the contrast (C) and the signal-to-noise ratio (SNR) of the reconstructed image. While the larger values of C suggest the usefulness of these superthermal states, the values of SNR do not improve at increasing the intensity light fluctuations, but remain the same as those exhibited by thermal light.

Lensless single-fiber ghost imaging

We demonstrate lensless single-fiber ghost imaging, which allows illumination and collection using a single optical fiber without a transmission-type system. Speckle patterns with relative coincidence degrees of 0.14 were formed by image reconstruction using improved differential ghost imaging. Employing fiber with a diameter of 105 µm, we achieved a spatial resolution of 0.05 mm in an observing area of 9mm2, at a working distance of 10 mm. Compared to a conventional neuroendoscope at a power density of 94mW/cm2, our imaging could be realized by extremely weak illumination at a laser power density of 0.10mW/cm2. Using our lensless single-fiber ghost imaging, with 30,000 speckle patterns and implementing a diffuser, we attained an average coincidence degree of 0.45.

Improved U-Net3+ With Spatial–Spectral Transformer for Multispectral Image Reconstruction

Multispectral image reconstruction, which aims to recover a three-dimensional (3D) spatial–spectral signal from a two-dimensional measurement in a spectral camera based on ghost imaging via sparsity constraint (GISC), has been attracting much attention recently. However, faced with abundant 3D spectral data, the reconstruction quality cannot meet the visual requirements. Based on the robust data processing capability of deep learning, a novel network called SSTU-Net3+ is constructed by improving U-Net3+ with a spatial–spectral transformer (SST). To enhance the feature representation of images during reconstruction, mixed pooling modules and new convolution processes are proposed to improve the performance of the encoder and decoder, with U-Net3+ as the backbone. To boost the quality of reconstructed images, with split and concatenate (Concat) operations, we construct SST modules by exploiting both spatial and spectral correlations of multispectral images to refine the spatial and spectral features. Furthermore, we employ the SST in the decoder to reconstruct the desired 3D cube. Given similar network parameters, experiments on GISC spectral imaging data show that, compared to convolutional neural network–based methods, the average peak signal-to-noise ratio of images reconstructed using SSTU-Net3+ is improved by 3%, the structural similarity is enhanced by 3%, and the spectral angle mapping is cut by 12%. Particularly, compared to differential ghost imaging and compressed sensing, the reconstruction quality of SSTU-Net3+ has been significantly improved. SSTU-Net3+ can process a large amount of 3D multispectral image data more efficiently and construct the target image more accurately than the abovementioned methods.

Arbitrary-matrix differential ghost imaging and reduction of the influence of reference path obstacles

Based on traditional ghost imaging (TGI) and differential ghost imaging (DGI), an arbitrary-matrix differential ghost imaging (ADGI) method, which can be adjusted by a self-defined matrix, is proposed. The image of stains on the lens of the reference path is successfully extracted, and it can be used to enhance the image clarity. This method can reduce the influence of occlusion in the reference path and the random interference in the image. We believe that this may provide convenience for the practical application of pseudo thermal ghost imaging.

Orthogonal-triangular decomposition ghost imaging

Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. However, there are still some obstacles for reconstructing images with high quality, especially in the case that the orthogonal measurement matrix is impossible to construct. In this paper, we propose a new scheme based on the orthogonal-triangular (QR) decomposition, named QR decomposition ghost imaging (QRGI) to reconstruct a better image with good quality. In the scheme, we can change the randomly non-orthogonal measurement matrix into orthonormal matrix by performing QR decomposition in two cases. (1) When the random measurement matrix is square, it can be firstly decomposed into an orthogonal matrix Q and an upper triangular matrix R . Then let the off-diagonal values of R equal to 0.0, the diagonal elements of R equal to a constant k, where k is the average of all values of the main diagonal, so the resulting measurement matrix can be obtained. (2) When the random measurement matrix is with full rank, we firstly compute its transpose, and followed with above QR operation. Finally, the image of the object can be reconstructed by correlating the new measurement matrix and corresponding bucket values. Both experimental and simulation results verify the feasibility of the proposed QRGI scheme. Moreover, the results also show that the proposed QRGI scheme could improve the imaging quality comparing to traditional GI (TGI) and differential GI (DGI). Besides, in comparison with the singular value decomposition ghost imaging (SVDGI), the imaging quality and the reconstruction time by using QRGI are similar to those by using SVDGI, while the computing time (the time consuming on the light patterns computation) is substantially shortened.

Influence of pulse characteristics on ghost imaging lidar system

A pulsed pseudo-thermal light source obtained using a rotating ground glass disk, spatial light modulator, or digital micromirror device is widely used in a ghost imaging (GI) lidar system. The property of the pulsed pseudothermal light field determines the reconstruction quality of the image in the GI lidar system, which depends on the pulse extinction ratio (PER) and pulse duty ratio. In this paper, pseudo-thermal light fields obtained at different pulse characteristics are given, taking into account the influence of the exposure time of the charge-coupled device (CCD) camera. The statistical distribution, contrast, and normalized intensity correlated function of the pseudo-thermal light field at different pulse characteristics are analyzed quantitatively for what we believe is the first time. Then the peak signal-to-noise ratio of the reconstructed image using a GI algorithm and a differential ghost imaging (DGI) algorithm is numerically simulated. The simulation results demonstrate that the PSNR decreases as the PER decreases, which is affected by the pulse duty ratio and the CCD exposure time. The deterioration of the reconstruction quality can be reduced by using a DGI algorithm or by shorting the exposure time of the CCD in the GI lidar system.

Ghost imaging enhancement for detections of the low-transmittance objects

The underwater environment is extremely complex and variable, which makes it difficult for underwater robots detecting or recognizing surroundings using images acquired with cameras. Ghost imaging as a new imaging technique has attracted much attention due to its special physical properties and potential for imaging of objects in optically harsh or noisy environments. In this work, we experimentally study three categories of image reconstruction methods of ghost imaging for objects of different transmittance. For high-transmittance objects, the differential ghost imaging is more efficient than traditional ghost imaging. However, for low-transmittance objects, the reconstructed images using traditional ghost imaging and differential ghost imaging algorithms are both exceedingly blurred and cannot be improved by increasing the number of measurements. A compressive sensing method named augmented Lagrangian and alternating direction algorithm (TVAL3) is proposed to reduce the background noise imposed by the low-transmittance. Experimental results show that compressive ghost imaging can dramatically subtract the background noise and enhance the contrast of the image. The relationship between the quality of the reconstructed image and the complexity of object itself is also discussed.

Signal-to-noise ratio analysis of computational distributed fiber-optic sensing.

In this work, we analyze the signal-to-noise ratio of the computational distributed fiber-optic sensing technique via differential ghost imaging in the time domain using the illumination pattern of Walsh-Hadamard sequences instead of random sequences. When only the white Gaussian noise is considered in the detection, both the theoretical and experimental results show that the computational method requires twice more number of averages compared to the conventional time-domain method in order to achieve the same level of signal-to-noise ratio. Since the computational approach is focusing on stationary measurement, doubling the measurement time can normally be acceptable in practice, but it can reduce the sampling rate requirement significantly compared to the conventional method, offering great advantage to simplify the data acquisition design in the distributed fiber-optic sensing system.

Quantum differential ghost microscopy

Quantum correlations become formidable tools for beating classical capacities of measurement. Preserving these advantages in practical systems, where experimental imperfections are unavoidable, is a challenge of the utmost importance. Here we propose and realize a quantum ghost imaging protocol stemming from the differential ghost imaging, a scheme elaborated so far in the limit of bright thermal light, particularly suitable in the relevant case of faint or sparse objects. The extension toward the quantum regime represents an important step as quantum correlations allow low-brightness imaging, desirable for reducing the absorption dose. Furthermore, we optimize the protocol in terms of signal-to-noise ratio, to compensate for the detrimental effects of detection noise and losses. We perform the experiment using spontaneous parametric down conversion light in a microscope configuration. The image is reconstructed exploiting nonclassical intensity correlation in the low photon flux regime, rather than photon pairs detection coincidences. On the one side, we validate the theoretical model and on the other we show the applicability of this technique by imaging biological samples.

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.

Singular value decomposition ghost imaging.

The singular value decomposition ghost imaging (SVDGI) is proposed to enhance the fidelity of computational ghost imaging (GI) by constructing a measurement matrix using singular value decomposition (SVD) transform. After SVD transform on a random matrix, the non-zero elements of singular value matrix are all made equal to 1.0, then the measurement matrix is acquired by inverse SVD transform. Eventually, the original objects can be reconstructed by multiplying the transposition of the matrix by a series of collected intensity. SVDGI enables the reconstruction of an N-pixel image using much less than N measurements, and perfectly reconstructs original object with N measurements. Both the simulated and the optical experimental results show that SVDGI always costs less time to accomplish better works. Firstly, it is at least ten times faster than GI and differential ghost imaging (DGI), and several orders of magnitude faster than pseudo-inverse ghost imaging (PGI). Secondly, in comparison with GI, the clarity of SVDGI can get sharply improved, and it is more robust than the other three methods so that it yields a clearer image in the noisy environment.

Iterative denoising of ghost imaging based on adaptive threshold method

Ghost imaging (GI) is an important technique in the fields of quantum imaging and classical optical imaging, and it can solve the problems which are difficult to solve by the traditional imaging techniques in the optically harsh environments. In this paper, we present the iterative denoising of GI based on an adaptive threshold method. This method is abbreviated as IDGI-AT, which takes the advantages of adaptive threshold, differential, binarization and iterative operation method, and can enhance image quality in GI. In addition, this method can reduce the number of measurements. As is well known, the enormous number of measurements and poor reconstruction quality are obstacles to the engineering application of GI. The correlation noise leads to low signal-to-noise ratio and low imaging efficiency in GI as well. Therefore, we establish a denoising model, which can reduce correlation noise and improve reconstruction quality. We first analyze the iterative denoising of ghost imaging (IDGI) theory, and use the adaptive threshold technique to calculate the ideal threshold associated with the correlation noise. It should be noted that the threshold can be obtained by this method under the condition without requiring prior knowledge of the object. Afterwards, we can construct the correlation noise in this denoising model. In the IDGI, the differential ghost imaging (DGI) image is taken as the initial iteration value. We use the adaptive threshold method, which is different from IDGI, to binarize the initial value of each iteration to make it closer to the original object's transmission coefficient. After three iterations, we can obtain a higher-quality reconstruction image. In order to demonstrate that the IDGI-AT is available, a GI experimental system with a pseudo-thermal light source is set up. The considerable simulation and experimental results show the advantage of our scheme in terms of removing reconstruction image background noise. Especially, the visual effects and peak signal-to-noise ratio values are improved in comparison with those from the traditional GI, DGI and IDGI. Besides, we demonstrate the role of binarization in our scheme. For a binary object, the iterative value binarization can achieve better image quality than that in the case without binarizing the iterative initial value. Therefore, this novel method is likely to provide an alternative mean for GI and further pave the way for the application fields of GI, such as lidar, biomedical engineering, etc.

Differential ghost imaging in time domain

Differential ghost imaging is attempted in time domain, i.e., temporal differential ghost imaging (TDGI), using pseudo-randomized light pulses and a temporal object consisting of no-return-to-zero bit patterns of varying duty. Evaluation of the signal-to-noise characteristics by taking into account errors due to false cross-correlation between the reference and the bucket detector readings indicates that the TDGI outperforms its non-differential counterpart, i.e., time-domain GI, in terms of consistently high and even duty-independent signal-to-noise ratios that are achieved.

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.

Sinusoidal ghost imaging.

We introduce sinusoidal ghost imaging (SGI), which uses 2D orthogonal sinusoidal patterns instead of random patterns in "computational ghost imaging" (CGI). Simulations and experiments are performed. In comparison with the"differential ghost imaging" algorithm that was used to improve the SNR of ghost imaging, results of SGI show about 3 orders of magnitude higher SNR, which can be reconstructed even with a much smaller number of patterns. More importantly, based on the results, SGI provides the great opportunity to generate innate processed images by predefined selection of patterns. This can speed up detection process considerably and paves the way for real applications.

Multiple speckle patterns differential compressive ghost imaging

Equipment requirement, quality of reconstructed image, and reconstruction time are important factors in the realization of thermal-light ghost imaging system. In this paper, we propose a new ghost imaging scheme with multiple speckle patterns, named multiple speckle patterns differential compressive ghost imaging scheme. In this scheme, the high temporal resolution requirements for detectors is reduced by continuously detecting multiple independent speckle patterns. We eliminate the background and other noises in ghost imaging system by using differential ghost imaging method. And the reconstruction time is effectively reduced simultaneously to improve the reconstructed image quality by introducing the compressive sensing techniques. Numerical results show that for the two-level grayscale “N” image, the mean square error, in using the proposed scheme with 8000 measurements, is reduced by 96.9%, and the peak signal to noise ratio has improved by 15.1 dB, in comparison with those using the original multiple speckle patterns ghost imaging scheme with 35000 measurements. For the eight-level grayscale “Pepper” image, the peak signal to noise ratio is enhanced by 11.4 dB. The proposed scheme also can decrease the requirements of detection equipment to improve the image quality, and reduce the reconstruction time. Therefore, it may have a broad application prospect.

多模差分鬼成像

多模差分鬼成像

Object reconstitution using pseudo-inverse for ghost imaging.

We propose a novel method for object reconstruction of ghost imaging based on Pseudo-Inverse, where the original objects are reconstructed by computing the pseudo-inverse of the matrix constituted by the row vectors of each speckle field. We conduct reconstructions for binary images and gray-scale images. With equal number of measurements, our method presents a satisfying performance on enhancing Peak Signal to Noise Ratio (PSNR) and reducing computing time. Being compared with the other existing methods, its PSNR distinctly exceeds that of the traditional Ghost Imaging (GI) and Differential Ghost Imaging (DGI). In comparison with the Compressive-sensing Ghost Imaging (CGI), the computing time is substantially shortened, and in regard to PSNR our method exceeds CGI on grayscale images and performs as well as CGI visually on binary images. The influence of both the detection noise and the accuracy of measurement matrix on PSNR are also presented.