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IEEE Asia-Pacific Conference on Antennas and Propagation

Entangled Light Quantum Imaging for Target Recognition Based on Denoising RestoreGAN

Huishi Xia, Yong Wang, Mu Zhou, Wei Nie, Quan Long, Li Fu

Published Nov 4, 2022• 0 citations
Publisher

Abstract

To address the poor recognition performance problem that the target after entangled light quantum imaging loses local appearance structure, this paper proposes an entangled light quantum imaging method for target recognition based on denoising RestoreGAN. Specifically, we first prepare the entangled photon pairs via spontaneous parametric down-conversion to achieve quantum imaging. Then, we design a conditional generative adversarial network, named RestoreGAN, to restore the missing edge contour structure of the target. Finally, the denoised image is input into the designed feature extraction network for target recognition and classification. Tested on the MNIST, the experimental results demonstrate that our denoising RestoreGAN can improve target recognition accuracy by 3.92 percentages.

DOI
10.1109/apcap56600.2022.10069753

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To address the poor recognition performance problem that the target after entangled light quantum imaging loses local appearance structure, this paper proposes an entangled light quantum imaging method for target recognition based on denoising RestoreGAN. Specifically, we first prepare the entangled photon pairs via spontaneous parametric down-conversion to achieve quantum imaging. Then, we design a conditional generative adversarial network, named RestoreGAN, to restore the missing edge contour structure of the target. Finally, the denoised image is input into the designed feature extraction network for target recognition and classification. Tested on the MNIST, the experimental results demonstrate that our denoising RestoreGAN can improve target recognition accuracy by 3.92 percentages.
Quantum imaging has the characteristics of non-locality and strong anti-interference ability, and it has received much attention. However, the target after entangled optical quantum imaging loses its local appearance structure, and it is difficult to be recognized accurately. Therefore, this paper proposes a denoise and target recognition method for entangled optical quantum imaging system. Specifically, we design the restore conditional generative adversarial network (RestoreCGAN) to restore and reconstruct the missing edge contour structure of the target. Then, we design the two-stream feature fusion convolutional neural network (TSFFCNet) to extract deep semantic features and shallow features for target recognition. The experimental results show that RestoreCGAN outperforms the state-of-the-art methods in terms of both PSNR and SSIM. Moreover, the recognition accuracy of the RestoreCGAN in combination with the TSFFCNet reaches 97.42%. This proves that the deep learning method is effective for denoise and target recognition of entangled optical quantum imaging system.
To address the problems of blurred contour and missed local image information of the entangled light quantum image, which leads to poor recognition performance, this paper proposes a target recognition method of entangled light quantum imaging based on a two-stream feature fusion network. Specifically, we first prepare the entangled photon pairs via spontaneous parametric down-conversion to achieve quantum imaging. Then, we design a two-stream feature fusion convolution network to recognize the target of the reconstructed quantum image. We test the MNIST handwritten digit images to verify the effectiveness of the proposed method.
Ghost imaging enables nonlocal image reconstruction and exhibits strong robustness against interference, but achieving high-fidelity recognition at ultra-low sampling rates remains challenging. Quantum machine learning offers a novel approach for efficient feature extraction on noisy medium-scale quantum devices; however, existing methods generally suffer from low recognition accuracy and weak noise resistance. This paper proposes a ghost imaging recognition method based on the simultaneous optimization of speckle patterns and quantum network parameters. By leveraging the mathematical equivalence between classical convolution and speckle-object dot product operations in ghost imaging, a speckle consistency regularization mechanism is introduced to achieve end-to-end joint optimization of optical coding and quantum feature extractors. A parallel 8-qubit quantum circuit employing block coding and a star-shaped entanglement structure is designed to extract higher-order features from bucket signals. Simulation results on the MNIST and Fashion-MNIST datasets show that at an ultra-low sampling rate of 1.5625%, the proposed framework achieves recognition accuracies of 90.1% and 81.7%, respectively, representing a 2.6% improvement over classical convolutional neural networks and a maximum improvement of 14.2% over traditional hybrid quantum machine learning models. This method also exhibits strong robustness to quantum noise and has been validated on a real optical ghost imaging system, achieving an average recognition accuracy of 84.8%. These results confirm that the joint optimization of speckle patterns and quantum network parameters provides a reliable and practical solution for low-sampling ghost imaging recognition.
As an important part of underwater optical technology, underwater imaging plays a crucial role in accurately capturing underwater targets and environmental features. Facing the challenges of complex ocean environments and severe photon attenuation, quantum imaging breaks through the limitations of traditional optical imaging technology by utilizing the characteristics of two-photon entanglement and time-space correlation, thus offering a new perspective on ocean turbulence. To this end, we propose a new underwater entangled-photon quantum imaging system. Specifically, we first construct an entangled photon quantum imaging physical model through ocean long-exposure turbulence and then exploit an entangled light coincidence imaging reconstruction method to image the target object. Furthermore, in response to the problem that ocean environment has a great impact on photons, we develop a photon capture probability method based on entangled photon pairs to reduce the impact of the ocean turbulence noise on imaging and improve the underwater target imaging resolution. We experimentally demonstrate the effectiveness of our method by showing that even in harsh ocean environments, quantum imaging performs superior resolution capabilities over traditional light source imaging techniques.
Three-dimensional (3D) imaging in low-light environments has broad application prospects. High-quality 3D imaging requires obtaining both the depth and reflectivity information of the target. With the rapid development of Quantum Information Technology (QIT), Quantum Imaging (QI) provides a new approach to achieve high-quality 3D imaging. However, traditional QI methods are limited to Two-dimensional (2D) imaging and cannot accurately extract the depth information from the target. These methods also suffer from insufficient precision in reflectivity calculations. Therefore, we introduce a novel quantum 3D imaging and reflectivity estimation method based on entangled photons. Specifically, we first utilize the spatiotemporal correlation characteristics of entangled photon pairs combined with coincidence measurement technology to accurately determine target depth. Second, we analyze the distribution of entangled photon numbers during free-space transmission to construct a photon-based detection probability model. Third, we use this model to derive the probability density function of coincidence counts. Finally, we estimate the surface reflectivity information through statistical analysis of coincidence counting. Experimental results demonstrate that the proposed method can accurately reconstruct both depth and reflectivity profiles of the target in low-light environments. These findings validate the effectiveness of our method and highlight the potential of quantum 3D imaging for target detection in low-light environments.
Quantum imaging systems produce images with distinctive noise patterns that conventional denoising algorithms cannot effectively process. We present an innovative neural network architecture that merges quantum physics principles with deep learning to address this challenge. Our hybrid approach adapts standard image processing techniques to handle quantum-specific noise while preserving critical image features. Experimental validation demonstrates a consistent 12.6% improvement in output quality compared to existing methods, with efficient performance on standard computing hardware. Additionally, the model exhibits strong generalization capabilities, achieving robust performance across varying noise levels. This advancement represents an important step toward practical quantum imaging applications in fields ranging from medical diagnostics to secure communications.