Progress of the computer-generated holography based on deep learning

Yixin Zhang; Kexuan Liu; Liangcai Cao

doi:10.1117/12.2642154

20 December 2022 Progress of the computer-generated holography based on deep learning

Yixin Zhang, Kexuan Liu, Liangcai Cao

Proceedings Volume 12318, Holography, Diffractive Optics, and Applications XII; 123180B (2022) https://doi.org/10.1117/12.2642154
Event: SPIE/COS Photonics Asia, 2022, Online Only

Abstract

Deep learning networks are widely used for optical problems solving in recent years. With the explosive developments of deep learning, learning-based computer-generated holography (CGH) has become an effective way to achieve real-time high-quality holographic display. Various learning-based methods have been proposed to accelerate the computation and improve the reconstruction quality. Deep neural networks (DNNs) have a great influence on the research of holography for their high quality and high computing speed. We focus on the rapid progress on DNN-based CGH in recent years and give our introduction to the principles of CGH as well as the structure of the deep neural networks frequently used in CGH, including U-Net, ResNet and GAN, etc. We introduce the developments of the learning-based CGH and express the expectation of the prospect.

Conference Presentation

Citation Download Citation

Yixin Zhang, Kexuan Liu, and Liangcai Cao "Progress of the computer-generated holography based on deep learning", Proc. SPIE 12318, Holography, Diffractive Optics, and Applications XII, 123180B (20 December 2022); https://doi.org/10.1117/12.2642154

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