Compressive Ghost Imaging Research Articles

Noise-robust and data-efficient compressed ghost imaging via the preconditioned S-matrix method

The design of the illumination pattern is crucial for improving imaging quality of ghost imaging (GI). The S-matrix is an ideal binary matrix for use in GI with non-visible light and other particles since there are no uniformly configurable beam-shaping modulators in these GI regimes. However, unlike widely researched GI with visible light, there is relatively little research on the sampling rate and noise resistance of compressed GI based on the S-matrix. In this paper, we investigate the performance of compressed GI using the S-matrix as the illumination pattern (SCSGI) and propose a post-processing method called preconditioned S-matrix compressed GI (PSCSGI) to improve the imaging quality and data efficiency of SCSGI. Simulation and experimental results demonstrate that compared with SCSGI, PSCSGI can improve imaging quality in noisy conditions while utilizing only half the amount of data used in SCSGI. Furthermore, better reconstructed results can be obtained even when the sampling rate is as low as 5%. The proposed PSCSGI method is expected to advance the application of binary masks based on the S-matrix in GI.

Cascade cryptographic key-distribution protocol over a public network based on snapshot compressive ghost imaging

Cascade cryptographic key-distribution protocol over a public network based on snapshot compressive ghost imaging

Hyperchaotic bilateral random low-rank approximation random sequence generation method and its application on compressive ghost imaging

Hyperchaotic bilateral random low-rank approximation random sequence generation method and its application on compressive ghost imaging

Computational ghost image encryption method based on sparse speckles

Optical encryption based on ghost imaging has the advantages of high-speed parallel processing and multi-dimensional information. However, in practical application, a large number of speckle patterns are required to achieve the encryption, resulting in a significant increase in transmission costs and low encryption efficiency. In this paper, a compressive sensing-based ghost imaging encryption method using sparse speckles is proposed. The sparse speckle used in this method has the characteristics of easy compression and greatly reducing the amount of encryption keys. According to calculation, the memory overhead of keys using sparse speckles can reduce to 2.44% of that using random speckles. And the decryption effect and anti-noise performance of this method are also verified better than that using random speckles by simulation and experiment results.

Joint Authentication Public Network Cryptographic Key Distribution Protocol Based on Single Exposure Compressive Ghost Imaging

In the existing ghost-imaging-based cryptographic key distribution (GCKD) protocols, the cryptographic keys need to be encoded by using many modulated patterns, which undoubtedly incurs long measurement time and huge memory consumption. Given this, based on snapshot compressive ghost imaging, a public network cryptographic key distribution protocol is proposed, where the cryptographic keys and joint authentication information are encrypted into several color block diagrams to guarantee security. It transforms the previous single-pixel sequential multiple measurements into multi-pixel single exposure measurements, significantly reducing sampling time and memory storage. Both simulation and experimental results demonstrate the feasibility of this protocol and its ability to detect illegal attacks. Therefore, it takes GCKD a big step closer to practical applications.

Robust compressed ghost imaging against environmental influence factors.

Ghost imaging based on sparse sampling is sensitive to the environmental influence factors frequently encountered in practice, such as instrumental drift and ambient light change, which could cause degradation of image quality. In this manuscript, we report a robust compressed sensing technique which could effectively reduce the influence of measurement errors on image quality. For demonstration purposes, we implement the proposed technique to ghost imaging, namely differential compressed sensing ghost imaging (DCSGI). By applying differential measurements n times, the first n Taylor expansion polynomials of the error could be eliminated in n-order DCSGI. It has been verified theoretically and experimentally that DCSGI works well with typical errors which exists in the realities of ghost imaging applications, while the conventional approach can hardly. In addition, the proposed technique may also replace conventional compressed sensing in other applications for anti-interference high-quality reconstruction.

Passive compressive ghost imaging with low rank clustering

Passive compressive ghost imaging with low rank clustering

Compressive color ghost imaging based on pseudo-inverse matrix

In this paper, a compressive color ghost imaging method based on pseudo-inverse matrix is proposed, which improves the quality of imaging result via post-processing the measurement matrix. For a color ghost imaging utilizing compressive sensing algorithm, if we treat a color image as a form of grayscale image and perform pseudo-inverse operation on the measurement matrix, a new measurement model will be established and the preliminary ghost imaging reconstruction result can be obtained by the compressive sensing algorithm, then the preliminary result is converted into a superposition of three channels to form the final color image. The feasibility of this method is proved by numerical simulation and physical experiment, and comparations among our method and the latest typical improvement methods, i.e., the singular value decomposition compressive ghost imaging and the pseudo-inverse ghost imaging, are conducted. The results show that our method can achieve the better quality of reconstructed color image with a high structure similarity beyond 0.8.

Passive compressive ghost imaging with low-rank optimization

High-quality passive ghost imaging with a low sampling rate can effectively improve the channel capacity utilization of optical imaging systems, but the current ghost imaging methods generally suffer from the problem of insufficient channel capacity utilization. We take advantage of the property that the matrix composed of nonlocal similar blocks of an image has a low rank, combine it as a priori information with the compressed sampling model of ghost imaging, establish a ghost imaging model based on low-rank optimization, and propose an alternating minimization solution method for this model. According to the simulation and experimental verification of the ghost imaging camera, our proposed ghost imaging method outperforms similar imaging methods in PSNR, SSIM, as well as other objective evaluation indexes. Among them, for the reconstruction of a typical scene at a sampling rate of 6.25 %, our method outperforms peer methods by 20.6 % for PSNR and 68.7 % for SSIM. Furthermore, we analyze the confidence intervals and uncertainties of different ghost imaging methods in the process of optical field decoding, and the experimental results show that our imaging method has a higher confidence level.

Compressed ghost imaging based on deep image prior using singular value decomposition

To improve the low-quality reconstruction of compressed ghost imaging (CGI) caused by non-negative measurement matrix, and avoid the requirements of large amount of training dataset and time-consuming training process, compressed ghost imaging based on deep image prior using singular value decomposition (SVDCGI-DIP) is proposed. The measurement matrix and corresponded measurements are first generated through numerical simulation and then simultaneously optimized with singular value decomposition without the change of optical setup of CGI. Secondly, deep image prior (DIP) method is applied to untrained generative network models to avoiding the long training process, and learned regularization is incorporated to further optimize network parameters to iteratively reconstruct the original image from only a few measurements of an image without training. Numerical experiments verify the superiority of our proposed SVDCGI-DIP method.

Bipolar compressive ghost imaging method to improve imaging quality.

Compressive ghost imaging (CGI) can effectively reduce the number of measurements required for ghost imaging reconstruction. In most cases, however, when using illumination patterns as measurement matrices, CGI has not demonstrated the ability to reconstruct high-quality images at an ultra-low sampling rate as perfect as claimed by compressive sensing theory. According to our analysis, the reason is that the non-negative nature of light intensity causes measurement matrix in compressive ghost imaging to be inconsistent with the essential requirements of good measurement matrix in compressive sensing theory, leading to low reconstruction quality. Aiming at this point, we propose a bipolar compressive ghost imaging method to improve the reconstruction quality of ghost imaging. The validity of the proposed method is proven by simulations and experiments.

Mutual structure ghost imaging under low sampling

The realization of high-quality imaging under low sampling is an effective way to solve the practical application of ghost imaging. In this paper, we present an advanced framework of compressed ghost imaging under low sampling. During the imaging process, the regularization and mutual structure filtering operations are performed alternately, which we call mutual structure ghost imaging (MSGI). In the joint filtering of our proposed scheme, the mutual-structural information contained in both the reference image and the target one is applied to enhance the capability of important edge preserving. Thus, high-resolution ghost imaging results can be obtained under low sampling. Moreover, we have adopted a fast-converging iterative format to obtain better imaging results with fewer iterations. Simulation and experimental results show that the proposed method can achieve high-quality imaging results from the random speckle patterns under low sampling and promote the practical process of ghost imaging.

Multiple-Image Reconstruction of a Fast Periodic Moving/State-Changed Object Based on Compressive Ghost Imaging

We propose a multiple-image reconstruction scheme of a fast periodic moving/state-changed object with a slow bucket detector based on compressive ghost imaging, named MIPO-CSGI. To obtain N frames of an object with fast periodic moving/state-changed, N random speckle patterns are generated in each cycle of the object, which are then used to illuminate the object one by one. The total energy reflected from the object is recorded by a slow bucket detector at each cycle time T. Each group with N random speckle patterns is programmed as one row of a random matrix, and each row of the matrix element corresponds to one measurement of the slow bucket detector. Finally, the compressive sensing algorithm is applied to the constructed matrix and bucket detector signals, resulting in the direct acquisition of multiple images of the object. The feasibility of our method has been demonstrated in both numerical simulations and experiments. Hence, even with a slow bucket detector, MIPO-CSGI can image a fast periodic moving/state-changed object effectively.

Correction to: Singular value decomposition compressed ghost imaging

Correction to: Singular value decomposition compressed ghost imaging

Optical image encryption with high efficiency based on variable-distance ghost imaging

In this paper, we propose an efficiency-enhanced optical image encryption system based on compressive computational ghost imaging (CCGI) with variable distance. Compressive sensing (CS) is known to be an effective algorithm to reduce the required number of measurements for a good reconstruction quality in cryptosystems based on computational ghost imaging (CGI). However, thousands of two-dimensional (2-D) random phase masks (RPMs) are still required as keys for one decrypted image in the conventional CCGI-based cryptosystems. This results in low efficiency of key transmission or storage. Hence, a novel optical cryptosystem based on variable-distance CCGI is proposed. In the proposed system, the distance from the RPM to the object can be shifted, and the same number of measurements can be realized by much less RPMs with various distances. It has been demonstrated that good reconstruction quality can be realized by only one RPM with various distances using the proposed approach. Compared to the conventional CCGI-based cryptosystems, many 2-D RPMs are saved for the same decryption quality in the proposed system, with a one-dimensional (1-D) vector consisting of various distances as a key instead. It is shown that the burden of key transmission or storage in the proposed system is reduced significantly. Meanwhile, the influence of the shifted distances on the feasibility, key sensitivity and the robustness against noise attack of the proposed cryptosystem has been investigated by simulation experiments.

Underwater compressive computational ghost imaging with wavelet enhancement.

We propose a compressive Hadamard computational ghost imaging (CGI) method to restore clear images of objects in the underwater environment. We construct an underwater CGI system model and develop a total variation regularization prior-based compressed-sensing algorithm for the CGI image reconstruction. We design a wavelet enhancement algorithm to further denoise and enhance the quality of the CGI image. We build an experimental setup and implement a series of experiments. The effectiveness and advantages of the proposed method are experimentally investigated. The results show that the proposed method can achieve clear imaging for underwater objects with a sub-Nyquist sampling ratio. The proposed method is helpful for improving the image quality of the underwater 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.

Protecting Compressive Ghost Imaging with Hyperchaotic System and DNA Encoding

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.

Imaging reconstruction comparison of different ghost imaging algorithms

As an indirect and computational imaging approach, imaging reconstruction efficiency is critical for ghost imaging (GI). Here, we compare different GI algorithms, including logarithmic GI and exponential GI we proposed, by numerically analysing their imaging reconstruction efficiency and error tolerance. Simulation results show that compressive GI algorithm has the highest reconstruction efficiency due to its global optimization property. Error tolerance studies further manifest that compressive GI and exponential GI are sensitive to the error ratio. By replacing the bucket input of compressive GI with different bucket object signal functions, we integrate compressive GI with other GI algorithms and discuss their imaging efficiency. With the combination between the differential GI (or normalized GI) and compressive GI, both reconstruction efficiency and error tolerance will present the best performance. Moreover, an optical encryption is proposed by combining logarithmic GI, exponential GI and compressive GI, which can enhance the encryption security based on GI principle.

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.