A Deep Learning-Based Target Recognition Method for Entangled Optical Quantum Imaging System

Quantum imaging has the characteristics of nonlocality 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 article proposes a denoise and target recognition method for entangled optical quantum imaging systems. 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 peak signal-to-noise ratio (PSNR) and structural similarity (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 systems.

Automated summary

This article proposes a method for denoise and target recognition of entangled optical quantum imaging systems. The RestoreCGAN is designed to restore and reconstruct the missing edge contour structure of the target, and the TSFFCNet is designed to extract deep semantic features and shallow features for target recognition. Experimental results show that RestoreCGAN outperforms the state-of-the-art methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity (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 systems.