Diffraction model-driven neural networks trained using multiscale frequency-domain loss for layer-based high-quality computer-generated holography

Qiwei Fang; Huadong Zheng; Junchang Peng; Zhen Wang

doi:10.1117/12.3036344

22 November 2024 Diffraction model-driven neural networks trained using multiscale frequency-domain loss for layer-based high-quality computer-generated holography

Qiwei Fang, Huadong Zheng, Junchang Peng, Zhen Wang

Author Affiliations +

Proceedings Volume 13240, Holography, Diffractive Optics, and Applications XIV; 1324029 (2024) https://doi.org/10.1117/12.3036344
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Conference Poster

Abstract

Deep learning provides an efficient and feasible solution for computer-generated holography (CGH), and learning-based CGH shows great potential in realizing real-time and high-quality holographic display. However, due to the difficulty of convolutional neural networks (CNNs) in learning cross-domain tasks, most existing learning-based algorithms still struggle to produce high-quality holograms. Here, we propose a diffraction model-driven neural network (M-Holo) that uses multi-scale frequency domain loss to train network parameters to produce high-quality phase-only holograms(POHs). M-Holo embedded multi-receptive-field (MRF) modules into complex amplitude-generating network encoders designed to improve the receptive field of neural network. In addition, the multi-scale frequency domain loss (MSFL) is also increased in the training process of M-Holo, and the abstract feature of multiple levels of the target image are learned in the frequency domain, which further restricts the spatial domain loss insensitive information. The generalization effect of M-Holo is verified by numerical simulation and optical experiment of grayscale and 3D images. M-Holo can effectively improve the quality of reconstructed images and suppress image artifacts.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiwei Fang, Huadong Zheng, Junchang Peng, and Zhen Wang "Diffraction model-driven neural networks trained using multiscale frequency-domain loss for layer-based high-quality computer-generated holography", Proc. SPIE 13240, Holography, Diffractive Optics, and Applications XIV, 1324029 (22 November 2024); https://doi.org/10.1117/12.3036344

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