In the process of quantum correlation imaging, the use of a fixed sampling mode will affect the imaging efficiency and image reconstruction quality of quantum correlation imaging, and different sampling matrices will have different effects on the imaging results. Aiming at the above problems, this paper proposes an adaptive compressed imaging method based on quantum correlation (ACI). The Digtial Micromirror Devices (DMD) specific sampling mode is designed by distinguishing the different texture details of the image, and the reference light is compressed and sampled by different sampling matrices. Finally, the reference light and the signal light are matched and counted and brought into the reconstruction algorithm to obtain the target image. The experimental results show that the method effectively shortens the imaging time and improves the imaging quality. At the same time, the Toeplitz matrix in the sampling matrix has the best effect. Therefore, this method can make good use of image texture features to improve the quality of quantum correlation imaging while reducing imaging time.
In the process of quantum correlation imaging, the use of a fixed sampling mode will affect the imaging efficiency and image reconstruction quality of quantum correlation imaging, and different sampling matrices will have different effects on the imaging results. Aiming at the above problems, this paper proposes an adaptive compressed imaging method based on quantum correlation (ACI). The Digtial Micromirror Devices (DMD) specific sampling mode is designed by distinguishing the different texture details of the image, and the reference light is compressed and sampled by different sampling matrices. Finally, the reference light and the signal light are matched and counted and brought into the reconstruction algorithm to obtain the target image. The experimental results show that the method effectively shortens the imaging time and improves the imaging quality. At the same time, the Toeplitz matrix in the sampling matrix has the best effect. Therefore, this method can make good use of image texture features to improve the quality of quantum correlation imaging while reducing imaging time.
In recent years, quantum information technology, as a subversive and strategic technology that has attracted much attention, acquires, transmits, and processes information by means of unique physical phenomena such as quantum entanglement, nonlocality, and non-cloning. Quantum imaging technology achieves non-local imaging of targets by extracting entangled light quantum information with temporal and spatial correlation characteristics. By using the anti-interference and anti-turbulence characteristics of quantum entangled states, quantum imaging technology can break through the physical limits of traditional imaging fields, thereby improving imaging resolution and sensitivity. The latest technological development in quantum imaging has prompted researchers to study high-resolution and fast quantum imaging based on the spatial correlation characteristics of entangled photon pairs. In this circumstance, we propose a novel quantum imaging method based on two-step adaptive sampling optimization and carry out experimental verification. In concrete terms, in order to solve the problem that the efficiency of entangled light quantum imaging is limited by Digital Micromirror Device (DMD) sampling, we select the two-step adaptive sampling optimization algorithm to reduce the influence of DMD scanning on imaging efficiency. At the same time, for the sake of solving the problem that the quality of entangled photon quantum imaging is limited by the accuracy of coincidence counting, we add delay difference calculation in the imaging process and correct the signal photon arrival time pulse sequence to ensure the accuracy of coincidence counting. Finally, we conduct a large number of experiments to evaluate the performance of the two-step adaptive sampling optimization algorithm and verify the superiority of the proposed entangled quantum imaging method practically.
A cost-efficient entangled light quantum imaging method is proposed to improve imaging speed while preserve imaging quality. First of all, the target image is sparsely processed by wavelet transform, and the random Bernoulli matrix is selected as the measurement matrix to ensure the retention of the effective information in the sparse signal. Then, the image is reconstructed by using the Total Variation Augmented Lagrangian Alternating Direction Algorithm (TVAL3) to improve reconstruction accuracy and computation efficiency. Finally, the reconstruction quality of images at different sampling rates is compared and analyzed, and the effectiveness of the proposed method is verified based on an actual optical path of entangled light quantum imaging.
Quantum correlated imaging technology adopts single-point intensity detecting,huge information storage,slow imaging speed,so faster image reconstruction algorithm was required.The simulation of samples with image reconstructing algorithm was based on statistical arithmetic and compressed sensing respectively.The inputs of the compressed sensing algorithm are sparse images which are calculated with discrete cosine transform(DCT) and Gauss random matrices,reconstructing image with orthogonal matching pursuit(OMP).The result shows that the correlated imaging algorithm based on compressed sensing can lessen the number of measurements and save data space and speed.Therefore,the study of quantum correlated imaging image reconstruction algorithm has great significance for lessening the number of samples and improving imaging speed.
In order to reduce the time overhead of quantum imaging, a new ranging-assisted quantum imaging method based on coincidence counting is proposed in this paper. Firstly, the Digital Micromirror Device (DMD) is used to select the ranging area in the imaging area, and photon arrival time sequences on signal and reference light paths are recorded by delay coincidence. Secondly, the second-order correlation curve is drawn according to the coincidence count values to obtain the delay difference of these two optical paths. This delay difference corrects the photon arrival time sequences corresponding to the imaging area. Finally, the coincidence counting is performed based on the corrected time sequences, and the quantum imaging result is obtained according to the coincidence count values. Experimental results show that the proposed method can greatly reduce the time overhead of quantum imaging.
Correlated imaging offers great potentiality, with respect to standard imaging, to obtain the imaging of objects located in optically harsh or noisy environment. It can solve the problems which are difficult to solve by conventional imaging techniques. Recently, it has become one of the hot topics in quantum optics. In this paper, we propose a new scheme of correlated imaging with differential correlated imaging based on compressive sensing, named differential compressive correlated imaging. The new scheme takes advantage of the high signal-to-noise ratio of the differential correlated imaging and low-imaging sampling frequency of the compressed sensing technique. In the scheme, we utilize the intensity of the thermal light, which is in line with the Gaussian distribution, as the measurement matrix of compressive sensing. We extract the differential object information as the image object information which could be recovered via orthogonal matching pursuit algorithm with high quality. By numerical simulations, we verify the proposed scheme. Here, we select the two gray-scale images, such as double-slit and NUPT, as well as the two multi-grayscale images (Lena and Boats) as the object. We take sampling 350 times in differential compressive correlated imaging for measurement. The numerical simulation results show that for the above image objects, the average mean-square error (MSE) over 10 times for the differential compressive correlated imaging scheme is reduced by 97.7%, 93.9%, 92.5% and 71.4% respectively with respect to that of the differential correlated imaging scheme. Moreover, compared with the compressive ghost imaging, the MSE value of the same double-slit in CDGI, as well as Lena and Boats under the same conditions, is reduced by 50.4%, 72.9% and 66.8% separately, which indicates that the compressive differential correlated imaging scheme can greatly improve the signal-to-noise ratio of the imaging, and significantly reduce the imaging time.
Quantum autoencoder, as a quantum machine learning algorithm, provides an important research direction for image compression. This paper proposes a quantum autoencoder specifically designed for image compression. We first use feature mapping to store images as quantum states and propose a new parameterized quantum circuit and a pre-training method. After pre-training, a reference state is determined and the circuit parameters are updated through further training. Finally, we experimentally validate the quantum autoencoder. For reconstructed 10-qubit images, the fidelity reaches 0.98 and the SSIM reaches 0.927. For reconstructed 10-qubit noisy images, the PSNR improves by 1.4[Formula: see text]dB. Simulation results demonstrate that the proposed quantum autoencoder efficiently compresses quantum images while also possessing denoising capabilities.