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IEEE Asia-Pacific Conference on Antennas and Propagation

Cost-efficient Entangled Light Quantum Imaging Based on Compressed Sensing

Zhongyin Hu, Mu Zhou, Wei Nie, Xiaolong Yang, Jingyang Cao

Published Nov 4, 2022• 5 citations
Publisher

Abstract

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.

DOI
10.1109/apcap56600.2022.10069859

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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.
This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and thereby the data acquisition time. The image reconstruction is formulated as a sparse total least square problem which is solved with an iterative algorithm. Our experiments show that, compared with existing methods, the new method can achieve a significant performance gain in terms of mean square error and peak signal⁻noise ratio.
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.
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.
Quantum imaging (QI) has some unique advantages, such as nonlocal imaging and enhanced space resolution. However, the quality of the reconstructed images and the time of data acquisition leave much to be desired. Based on the framework of compressive sensing, we propose an optimization criterion for high-quality QI whereby total variation restriction is specifically utilized for noise suppression. The corresponding reported algorithm uses a combination of a greedy strategy and the interactive reweight least-squares method. The simulation and the actual imaging experiment both show a significant improvement of the proposed algorithm the over previous imaging method.
Entangled photon quantum imaging technology has become very promising for marine imaging due to its high resolution, anti-turbulence, and anti-interference properties. The turbulence and optical attenuation caused by oceanic water would impose severe limitations on conventional imaging equipment. Quantum imaging based on entangled light sources could make it possible to overcome the constraints of low imaging resolution, large maritime environmental impact and low confidentiality in traditional underwater target imaging. Hence, the recent technological developments in terms of quantum imaging in free space motivate our investigation into high-resolution quantum imaging based on spatially correlated properties of entangled photon pairs in a marine environment. Moreover we propose and design an innovative underwater quantum imaging optical path for experimental verification. We conduct extensive experiments to assess the effectiveness of our proposed quantum imaging approach in marine environments. In order to solve the problem of the slow quantum imaging process, we propose an innovative compressive sensing reconstruction algorithm. Experiments show that the compressive sensing method could be utilized to reduce the imaging time. Finally, we propose an underwater quantum target identification method based on deep learning, further providing the possibility for underwater quantum target identification of entangled light sources.
Quantum imaging based on entangled light sources exhibits enhanced background resistance compared to conventional imaging techniques in low-light conditions. However, direct imaging of dynamic targets remains challenging due to the limited count rate of entangled photons. In this paper, we propose a quantum imaging method based on quantum compressed sensing that leverages the strong correlation characteristics of entangled photons and the randomness inherent in photon pair generation and detection. This approach enables the construction of a compressed sensing system capable of directly imaging high-speed dynamic targets. The results demonstrate that our system successfully achieves imaging of a target rotating at a frequency of 10 kHz, while maintaining an impressive data compression rate of 10 −6 . This proposed method introduces a pioneering approach for the practical implementation of quantum imaging in real-world scenarios.