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Wu, H., Wang, R., Zhao, G., Xiao, H., Wang, D., Liang, J., Tian, X., Cheng, L. and Zhang, X. (2020) Subnyquist Computational Ghost Imaging with Deep Learning. Optics Express, 27, 3846-3853.
https://doi.org/10.1364/OE.386976

has been cited by the following article:

• TITLE: Image Reconstruction of Ghost Imaging Based on Improved Generative Adversarial Networks

  AUTHORS: Xu Chen

  KEYWORDS: Generative Adversarial Networks, Ghost Imaging, Image Reconstruction

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.10 No.4, April 12, 2022

  ABSTRACT: In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.