Ghost imaging enhancement for detections of the low-transmittance objects

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.

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.

Ghost imaging of the low or high frequency based on the corresponding spatial-frequency of the reference pattern

Traditional ghost imaging applies correlated algorithms to reconstruct the image of an object. However, it fundamentally requires some spatial distributions of the correlated light beam, e.g. random illumination, which hardly exists in reality. Here, different from the localized analysis used in the traditional ghost imaging, a spatial and temporal global analysis of the whole measurements is proposed. Therefore, we demonstrate a new ghost imaging modality, called adaptive ghost imaging (AGI), that utilizes the difference of successive frames as the correlation pattern to generate the image. As a result, AGI can work with any varying illuminations including, but not limited to, random illumination. We believe that AGI will make the ghost imaging easier, more applicable and closer to reality.

In this paper, self-modulated ghost imaging (SMGI) in a surrounded scattering medium is proposed. Different from traditional ghost imaging, SMGI can take advantage of the dynamic scattering medium that originally affects the imaging quality and generate pseudo-thermal light through the dynamic scattering of free particles’ Brownian motion in the scattering environment for imaging. Theoretical analysis and simulation were used to establish the relationship between imaging quality and particle concentration. An experimental setup was also built to verify the feasibility of the SMGI. Compared with the reconstructed image quality and evaluation indexes of traditional ghost imaging, SMGI has better image quality, which demonstrates a promising future in dynamic high-scattering media such as dense fog and turbid water.

In practical thermal light ghost imaging, the nonuniform intensity distribution of the thermal light applied causes distortion of the retrieved image. We propose uniformly weighted arithmetics, including uniformly weighted ghost imaging and uniformly weighted differential ghost imaging (UWDGI), to improve the imaging quality of ghost imaging with nonuniform thermal light fields. Two kinds of nonuniform thermal light fields are discussed, including planar Gaussian distributed thermal light and near-field three-dimensional thermal light. The retrieved image using a uniformly weighted arithmetic has a lower distortion than that using the corresponding traditional ghost imaging arithmetic. In addition, the best imaging quality is achieved with the UWDGI arithmetic.

For lower sampling rate and better imaging quality, a compressive adaptive ghost imaging is proposed by adopting the sharing mechanism and fellow relationship in the wavelet tree. The sharing mechanisms, including intrascale and interscale sharing mechanisms, and fellow relationship are excavated from the wavelet tree and utilized for sampling. The shared coefficients, which are part of the approximation subband, are localized according to the parent coefficients and sampled based on the interscale sharing mechanism and fellow relationship. The sampling rate can be reduced owing to the fact that some shared coefficients can be calculated by adopting the parent coefficients and the sampled sum of shared coefficients. According to the shared coefficients and parent coefficients, the proposed method predicts the positions of significant coefficients and samples them based on the intrascale sharing mechanism. The ghost image, reconstructed by the significant coefficients and the coarse image at the given largest scale, achieves better quality because the significant coefficients contain more detailed information. The simulations demonstrate that the proposed method improves the imaging quality at the same sampling rate and also achieves a lower sampling rate for the same imaging quality for different types of target object images in noise-free and noisy environments.

Recent researches showed that the traditional ghost imaging (TGI) using pseudo-thermal light can be affected by the solid scattering material placed between the object and the light source. This scattering case might also affect the computational ghost imaging (CGI) and is considered in this paper. Different from the TGI system modeled by Huygens–Fresnel theory in continuous coordinates, in CGI the use of computer-generated patterns with the controllable size ( $d$ ) of each pixel discretizes the object space into the $d$ -based pixel-grids. Therefore, how the CGI system is affected by the scatters depends on the distribution of the scattered light on these discretized grids instead of in continuous coordinates, which has not been explored. In this paper, we demonstrate that the “anti-scattering” capacity of CGI is governed by the discrete point-scattering-function (DPSF) that is determined by $\frac{{w_{0}}}{d}$ ( $w_{0}$ : the width of point-scattering-function of the scatters). We show that the blurry effect on the reconstructed image is visible as $d\ll\frac{{w_{0}}}{2}$ , but can be highly restrained as $d$ increasing to be comparable to $\frac{w_{0}}{2}$ or larger. Furthermore, $d$ determines the spatial resolution power of CGI. In the given CGI system, the minimum $d$ of $\sim\frac{{w}_{0}}{5}$ could be achieved with maintaining the behavior of “scattering-free”.

As computational ghost imaging is widely used in the military, radar, and other fields, its security and efficiency became more and more important. In this paper, we propose a compressive ghost imaging encryption scheme based on the hyper-chaotic system, DNA encoding, and KSVD algorithm for the first time. First, a 4-dimensional hyper-chaotic system is used to generate four long pseudorandom sequences and diffuse the sequences with DNA operation to get the phase mask sequence, and then N phase mask matrixes are generated from the sequences. Second, in order to improve the reconstruction efficiency, KSVD algorithm is used to generate dictionary D to sparse the image. The transmission key of the proposed scheme includes the initial values of hyper-chaotic and dictionary D, which has plaintext correlation and big key space. Compared with the existing compressive ghost imaging encryption scheme, the proposed scheme is more sensitive to initial values and more complexity and has smaller transmission key, which makes the encryption scheme more secure, and the reconstruction efficiency is higher too. Simulation results and security analysis demonstrate the good performance of the proposed scheme.

Foveated ghost imaging based on deep learning

Compressive ghost imaging (CSGI) combines structured illumination and a bucket detector for obtaining the light intensity signal from an unknown object. Light fluctuations are generated by few measurements and then an image is reconstructed using optimization algorithms such as compressive sensing (CS) by finding its sparse representation. The measured light fluctuations are not sensitive to scattering degradation. Consequently, an absorbing object completely embedded in a scattering media can be imaged. To speed up the sequential loading of illumination patterns on a digital micromirror device and achieve data compression, the sampling number should be reduced because more than 90% of the time in CS reconstruction is usually spent in getting a frame of image. In this study, we propose a novel strategy to realize a speedy and reliable reconstruction procedure for obtaining a high image quality by using prior knowledge during the acquisition of light intensity signal in CSGI. The prior knowledge is established by extracting the features of a local target area, which is designed by descattering of images with extra noise using fast Fourier single-pixel imaging. The proposed method facilitates a reliable image quality even under the reduction in the compression ratio, thereby overcoming the limitation of the dependence of sampling ratio on the image quality in CSGI.

Ghost imaging (GI) is thought of as a promising imaging method in many areas. However, the main drawback of GI is the huge measurement data and low signal-to-noise ratio. In this paper, we propose a novel mask-based denoising scheme to improve the reconstruction quality of GI. We first design a mask through the maximum between-class variance (OTSU) method and construct the measurement matrix with speckle patterns. Then, the correlated noise in GI can be effectively suppressed by employing the mask. From the simulation and experimental results, we can conclude that our method has the ability to improve the imaging quality compared with traditional GI method.

Speckle patterns arise when coherent light reflects from rough surfaces or propagates through complex scattering media, such as disordered materials or multimode fibers. These patterns exhibit a random granular structure, with their statistical properties typically following Rayleigh statistics. The probability density function (PDF) of the complex field conforms to a circular Gaussian distribution, while the intensity PDF follows a negative exponential distribution. Wavefront shaping techniques can modify these statistical properties by controlling wavefront intensity, polarization, or phase [1], enabling diverse applications, including advanced imaging, cryptography, and optical communications. Compressive ghost imaging (CGI) leverages speckle patterns for image reconstruction using a bucket detector instead of a traditional camera [2]. This technique relies on compressive sensing, which exploits object sparsity to achieve accurate image reconstruction with fewer measurements. CGI is particularly advantageous in challenging environments, such as imaging through highly scattering or dynamic media.

With the rapid development of computer technology, information security has attracted more and more attention. It is not only related to the information and property security of individuals and enterprises, but also to the security and social stability of a country. Identity authentication is the first line of defense in information security. In authentication systems, response time and security are the most important factors. An optical authentication technology based on compressive ghost imaging with QR codes is proposed in this paper. The scheme can be authenticated with a small number of samples. Therefore, the response time of the algorithm is short. At the same time, the algorithm can resist certain noise attacks, so it offers good security.

Ghost imaging (GI) has characteristics that make it promising for applications in life sciences and other fields, such as its high sensitivity and strong anti-interference compared with traditional imaging. This paper presents a new approach for online angiography using GI. Two signals are correlation-calculated to detect the object image: one of them is the random light field generated by a computer, and the other enters the optical fiber path after being transmitted to the detected object via a modulator. A new approach for the real-time imaging of intravascular flow, vascular wall structures, and components of atherosclerotic plaque is proposed, which has the advantages of a high sensitivity and anti-interference.