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Image Reconstruction of Ghost Imaging Based on Improved Generative Adversarial Networks
2022
Journal of Applied Mathematics and Physics
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
doi:10.4236/jamp.2022.104076
fatcat:mc7osyhsa5gsbfrvt5zvxmflyy