Conventional Ghost Imaging Research Articles

High performance multi-image encryption based on temporal-spatial double random ghost coding

A high performance multi-image encryption method is presented by using the temporal-spatial double random ghost coding (TSDRGC). As an original idea in optical encryption, TSDRGC employs the chaotic lasers as light sources instead of conventional lasers. The random fluctuations of the chaotic laser can be seen as the new temporal security keys introduced into the cryptosystem, except the random illumination patterns used usually in the conventional ghost imaging as a set of spatial security keys. In the encryption process, all the images can be optically encoded in parallel and the time consumed by the encryption process does not increase compared to encrypting an image alone. Meanwhile, a decryption algorithm is proposed to achieve high quality decryption of each image independently while reducing the bit-width requirement. The simulations demonstrate the excellent performance of the scheme in terms of security, capability and robustness.

Elimination of signal amplitude disturbance in ghost imaging using an auxiliary laser channel.

Ghost imaging can be used to detect objects in a nonstationary environment or in the presence of variable ambient light, making it attractive when conventional imaging methods are ineffective. However, the conventional ghost imaging algorithm is susceptible to temporal fluctuations in the detected signal. In this work, we propose a polarization-multiplexed auxiliary laser channel propagating along the same optical path with the main one. The signal in the auxiliary channel is used as a reference and allows the elimination of signal disturbance. A quantitative analysis and comparison of the proposed method's performance to the high-pass filtering method are demonstrated. For an illumination pattern refresh rate of 10Hz, effective suppression of bucket signal fluctuations has been experimentally demonstrated. For a disturbance frequency from 1Hz to 10Hz, the auxiliary channel method demonstrated a ghost image Pearson correlation coefficient (PCC) of not less than 0.70, while the high-pass filtering method showed a PCC sharp drop from 0.65 to 0.02.

Self-corrected orthonormalized ghost imaging through dynamic and complex scattering media

In this Letter, we report a setup design to realize high-visibility orthonormalized ghost imaging (GI) with self-correction through dynamic and complex scattering media at low sampling ratios. With the design of a parallel detection, a mismatch between illumination patterns and intensity measurements is corrected. Gram–Schmidt orthonormalization is further applied to the illumination patterns and corrected intensities in order to implement high-visibility GI through dynamic and complex scattering media at low sampling ratios. It is experimentally demonstrated that the proposed self-correction and orthonormalization enable high-visibility and high-efficiency GI through dynamic and complex scattering media at low sampling ratios. The proposed method offers a promising alternative to overcome the challenge faced by conventional GI in implementing high-visibility object reconstruction through dynamic and complex scattering media at low sampling ratios.

Ghost diffraction: A spatial statistical approach

We report a new method to reconstruct a two-dimensional object in Ghost diffraction scheme using a spatial statistical optics approach. This is implemented by using a static diffuser rather than a pseudo thermal light source generated with a rotating diffuser as used in conventional ghost imaging systems. The experimental implementation makes use of spatial ergodicity and spatial stationarity for spatially distributed random fields by following an analogy between time average and space average. The ensemble average is replaced by space average under the condition of spatial stationarity and ergodicity. A strategy to realize the Ghost diffraction scheme through spatial intensity correlation with a phase retrieval algorithm permits reconstruction of the transparency.

Temporally and spatially variant-resolution illumination patterns in computational ghost imaging

Conventional computational ghost imaging (CGI) uses light that carries a sequence of patterns with uniform resolution to illuminate an object and then performs correlation calculations based on the light intensity value reflected by the object and the preset patterns to obtain object images. CGI requires numerous measurements to obtain high-quality images, especially if high-resolution images are to be obtained. To solve this problem, we developed temporally variable-resolution illumination patterns, replacing the conventional uniform-resolution illumination patterns with a sequence of patterns of different imaging resolutions. In addition, we propose combining temporally variable-resolution illumination patterns and spatially variable-resolution structures to develop temporally and spatially variable-resolution (TSV) illumination patterns, which not only improves the imaging quality of the region of interest (ROI), but also reduces the effect of noise, making up for the shortcomings of temporally variable-resolution patterns. The methods using the proposed illumination patterns are verified by simulations and experiments compared with uniform-resolution computational ghost imaging (UCGI). For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than UCGI but is less robust to noise. The method using TSV illumination patterns has better imaging quality in the ROI than the method using temporally variable-resolution illumination patterns and UCGI with the same number of measurements. We also experimentally verify that the method using TSV patterns has better imaging performance when applied to higher resolution imaging. The proposed methods are expected to solve the current CGI problem that hinders high-resolution and high-quality imaging.

High-resolution ghost imaging through complex scattering media via a temporal correction

In this Letter, we propose high-resolution ghost imaging (GI) through complex scattering media using temporal correction. We provide evidence that the theoretical description about GI based on spatially correlated beams is still incomplete and cannot work in complex scenarios. We complete the description of temporal correction of beam correlations in GI. The optical experiments demonstrate that high-resolution ghost images can always be retrieved by using the rectified temporally corrected beam correlation algorithm even in complex, dynamic, and highly strong scattering environments where conventional GI cannot work. By using the proposed method, the quality of the retrieved ghost images through complex scattering media can be enhanced effectively as the number of realizations increases, which cannot be achieved by conventional GI. The established general framework provides optical insights beyond the current understanding of GI, and the rectified theory and experimental results would represent a key step toward applications of GI over a wide range of free-space wave propagation environments.

0.8% Nyquist computational ghost imaging via non-experimental deep learning

We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. The conventional computational ghost imaging results, deep learning-based ghost imaging results with white and pink noise are compared under multiple sampling ratios at different noise conditions. The experiments are done with digits, English letters, and Chinese characters. We show that the proposed scheme can provide high-quality images with a sampling ratio as low as 0.8% even when the object is outside the training dataset and robust to noisy environments. The method can be applied to a wide range of applications, including those requiring a low sampling ratio, fast reconstruction, or experiencing strong noise interference.

Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network

There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging. In this article, we report a turbulence-immune CGI method based on a multi-scale generative adversarial network (MsGAN). Here, the conventional CGI framework is not changed, but the conventional CGI coincidence measurement algorithm is optimized by an MsGAN. Thus, the satisfactory ghost image can be reconstructed by training the network, and the visual effect can be significantly improved.

Optimization of retina-like illumination patterns in ghost imaging

Ghost imaging (GI) reconstructs images using a single-pixel or bucket detector, which has the advantages of scattering robustness, wide spectrum, and beyond-visual-field imaging. However, this technique needs large amounts of measurements to obtain a sharp image. Numerous methods are proposed to overcome this disadvantage. Retina-like patterns, as one of the compressive sensing approaches, enhance the imaging quality of the region of interest (ROI) while maintaining measurements. The design of the retina-like patterns determines the performance of the ROI in the reconstructed image. Unlike the conventional method to fill in ROI with random patterns, optimizing retina-like patterns by filling in the ROI with the patterns containing the sparsity prior of objects is proposed. The proposed method is then verified by simulations and experiments compared with conventional GI, retina-like GI, and GI using patterns optimized by principal component analysis. The method using optimized retina-like patterns obtains the best imaging quality in ROI among other methods. Meanwhile, the good generalization capability of the optimized retina-like pattern is also verified. The feature information of the target can be obtained while designing the size and position of the ROI of retina-like patterns to optimize the ROI pattern. The proposed method facilitates the realization of high-quality GI.

Single-arm ghost imaging via conditional generative adversarial network

In this study, we develop a single-arm ghost imaging (GI) framework based on a conditional generative adversarial network (cGAN) to improve the image quality and extend the application scenarios of GI. A set of one-dimensional (1D) bucket signals generated by a single-arm GI system and their corresponding ground-truth counterparts is employed to train the cGAN. This allows us to reconstruct a low-noise image from a new 1D bucket signal, while the sequence of random patterns is unnecessary. The results show that the proposed method significantly improves the image quality relative to the conventional GI methods.

Time division ghost imaging.

In the conventional ghost imaging, it requires to illuminate a large number of patterns in order to reconstruct a good quality image under a low signal-to-noise ratio. We propose a new method so called time division ghost imaging to improve the quality of the image in noisy environment. In this procedure, the total number of patterns in the calculation process of the correlation function are divided into the sub-units with fewer illuminated patterns. Then one calculates the correlation for each sub-unit, and synthesizes the intermediate images obtained at each sub-unit. The validation and effectiveness of this method are confirmed by simulation and experiment, showing the robustness to noise.

Hybrid cryptosystem based on plaintext related computational ghost imaging encryption and elliptic curve algorithm

A hybrid cryptosystem based on plaintext-related ghost imaging and elliptic curve public-key cryptographic algorithm (ECC) is proposed in this paper. Thereinto, the ECC algorithm is used to protect and distribute session keys and the optical encryption system with session keys is used to encrypt and decrypt images. Thousands of phase-only mask keys can be replaced by parameter keys in this method, which successfully reduces the pressure of key storage. With dynamic updating session keys, the attacks are effectively prevented and the security resilience is enhanced. In addition, the utilization of an ECC cryptosystem solves the inevitable problem of key management and dispatch in conventional ghost imaging encryption system. The feasibility, security and robustness of the method are further analyzed through computer simulations.

Plaintext-related multiple-image encryption based on computational ghost imaging

ABSTRACTThe traditional ghost imaging encryption system has high pressure in key transmission and management for involving a series of random phase-only masks (POMs) as keys. And conventional ghost imaging encoding is vulnerable to a chosen-plaintext attack owing to the linearity of the system. To address these problems, a plaintext-related ghost imaging encryption system which can reduce the key size and ensure the security of the system was proposed in this study. It uses parameter keys and plaintext information to generate all random POMs for encryption of plaintext. POMs used to encrypt images depend on both parameter keys and plain image, thus protecting information from cracking by the chosen-plaintext. At the same time, a multiple-image encryption method based on plaintext-related ghost imaging encryption was developed to improve the encryption efficiency of the system. The proposed method is the characteristic of strong multiple-image encryption ability, high security and enough simple structure.

Instant ghost imaging: improving robustness for ghost imaging subject to optical background noise

Ghost imaging (GI) is an imaging technique that uses the second-order correlation between two light beams to obtain the image of an object. However, standard GI is affected by optical background noise, which reduces its practical use. We investigated the robustness of an instant ghost imaging (IGI) algorithm against optical background noise and compare it with the conventional GI algorithm. Our results show that IGI is extremely resistant to spatiotemporally varying optical background noise that can change over a large range. When the noise is large in relation to the signal, IGI will still perform well in conditions that prevent the conventional GI algorithm from generating an image because IGI uses signal differences for imaging. Signal differences are intrinsically resistant to common noise modes, so the IGI algorithm is strongly robust against noise. This research is of great significance for the practical application of GI.

Instant ghost imaging: algorithm and on-chip implementation.

Ghost imaging (GI) is an imaging technique that uses the correlation between two light beams to reconstruct the image of an object. Conventional GI algorithms require large memory space to store the measured data and perform complicated offline calculations, limiting practical applications of GI. Here we develop an instant ghost imaging (IGI) technique with a differential algorithm and an implemented high-speed on-chip IGI hardware system. This algorithm uses the signal between consecutive temporal measurements to reduce the memory requirements without degradation of image quality compared with conventional GI algorithms. The on-chip IGI system can immediately reconstruct the image once the measurement finishes; there is no need to rely on post-processing or offline reconstruction. This system can be developed into a realtime imaging system. These features make IGI a faster, cheaper, and more compact alternative to a conventional GI system and make it viable for practical applications of GI.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning is proposed. It can image the stationary target scene continuously along the scanning direction by taking advantage of the relative movement between the platform and the target scene. Compared to conventional ghost imaging LiDAR that requires multiple speckle patterns staring the target, ghost imaging LiDAR via sparsity constraints using push-broom scanning not only simplifies the imaging system, but also reduces the sampling number. Numerical simulations and experiments have demonstrated its efficiency.

Interaction-free ghost-imaging of structured objects.

Quantum - or classically correlated - light can be employed in various ways to improve resolution and measurement sensitivity. In an "interaction-free" measurement, a single photon can be used to reveal the presence of an object placed within one arm of an interferometer without being absorbed by it. With a technique known as "ghost-imaging", entangled photon pairs are used for detecting an opaque object with significantly improved signal-to-noise ratio while preventing over-illumination. Here, we integrate these two methods to obtain a new imaging technique which we term "interaction-free ghost-imaging" (IFGI). With this new technique, we reduce photon illumination on the object by up to 26.5% while still maintaining at least the same image quality of conventional ghost-imaging. Alternatively, IFGI can improve image signal-to-noise ratio by 18% when given the same number of interacting photons as in standard ghost-imaging. IFGI is also sensitive to phase and polarisation changes of the photons introduced by a structured object. These advantages make IFGI superior for probing light-sensitive materials and biological tissues.

Demonstration of correlated imaging enhancement with divergence enlargement algorithms

Abstract We propose advanced image enhancement algorithms for conventional ghost imaging. The algorithms are based on modulating the bucket signals to enlarge their divergence. In this paper, we adopt three mathematical functions: power function like f ( B ) = B n , exponential function like f ( B ) = exp ( p ⋅ B ) , and self modulation function like f ( B ) = B q ⋅ B , where B is the bucket intensity in the object path. Our experimental results demonstrate the enhancement of the contrast of the correlated images with these algorithms.

Nonrandom Microwave Ghost Imaging

Ghost imaging (GI) is a novel imaging method originated from quantum and optical areas. Due to its nonlocal and nonscanning features, the GI has drawn much attention from different research communities and been adopted into microwave imaging scenarios recently. However, most of the current microwave GI schemes reported in the literature are mainly theoretical approaches. On the one hand, this is because conventional microwave GI systems require a large number of antennas transmitting random modulated signals which is quite challenging in practice. On the other hand, unlike optical GI where charge-coupled devices are used to collect the spatial information of background light fields, current microwave GI schemes can only rely on the estimation of the background microwave fields. Consequently, the reconstruction of objects will suffer from extra perturbations due to estimation errors. Therefore, in order to reduce the complexity and difficulty in the practical implementation of microwave GI, we proposed a novel microwave GI scheme based on nonrandom electromagnetic (EM) fields in this paper. By applying purposely designed EM fields to illuminate the imaging scenario, both the requirement of randomness and the involvement of background microwave field estimations in the framework of current microwave GI have been removed. Thus, its mathematical imaging model has been simplified from a total-least-square (LS) problem to an ordinary-LS one. In addition, by the employing binary orthogonal matrix as the reference for generating the background EM fields, the reconstructed image can be obtained directly without iterative refinement. Numerical simulations show that the proposed nonrandom microwave GI can effectively reconstruct objects with different profiles under different signal-to-noise-ratio conditions. It also shows that both the reconstruction performance and complexity of the proposed method are superior to conventional microwave GI. Besides, similar comparison results can be observed when compressive samplings are applied.

Noise reduction in computational ghost imaging by interpolated monitoring.

An interpolation computational ghost imaging (ICGI) method is proposed and demonstrated that is able to reduce the noise interference from a fluctuating source and background. The noise is estimated through periodic illuminations by a specific assay pattern during sampling, which is then used to correct the bucket detector signal. To validate this method simulations and experiments were conducted. Light source intensity and background lighting were randomly varied to modulate the noise. The results show that good quality images can be obtained, while with conventional computational ghost imaging (CGI) the reconstructed object is barely recognizable. The ICGI method offers a general approach applicable to all CGI techniques, which can attenuate the interference from source fluctuations, background light noise, dynamic scattering, and so on.