Unsupervised Fourier-inspired neural network for real-time and high-fidelity computer-generated holography

Zhenxing Dong, Chao Xu, Yuye Ling, Yan Li & Yikai Su
Learning-based computer-generated holography (CGH) algorithms appear as novel alternatives to generate phase-only holograms. However, most existing learning-based approaches underperform their iterative peers in terms of display quality. Here, we propose an unsupervised Fourier-inspired neural network that could generate phase-only holograms in real time and obtain high-fidelity reconstructed images. Both simulation and experiment were performed to showcase its capability. By explicitly leveraging Fourier transforms within the neural network architecture, global features of the holograms are more...
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