3D-HoloNet: fast, unfiltered, 3D hologram generation with camera-calibrated network learning

被引：1

作者：

Zhou, Wenbin ^{[1
]}

Qu, Feifan ^{[1
]}

Meng, Xiangyu ^{[1
]}

Li, Zhenyang ^{[1
]}

Peng, Yifan ^{[1
]}

机构：

[1] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam Rd, Hong Kong, Peoples R China

来源：

OPTICS LETTERS | 2025年 / 50卷 / 04期

关键词：

Computer generated holography - Computer graphics equipment - Deep neural networks - Digital storage - Electron holography - Holographic displays - Image quality - Image reconstruction - RGB color model - Three dimensional displays;

D O I：

10.1364/OL.544816

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Computational holographic displays typically rely on time-consuming iterative computer-generated holographic (CGH) algorithms and bulky physical filters to attain high-quality reconstruction images. This trade-off between inference speed and image quality becomes more pronounced when aiming to realize 3D holographic imagery. This work presents 3D-HoloNet, a deep neural network- empowered CGH algorithm for generating phase-only holograms (POHs) of 3D scenes, represented as RGB-D images, in real time. The proposed scheme incorporates a learned, camera-calibrated wave propagation model and a phase regularization prior into its optimization. This unique combination allows for accommodating practical, unfiltered holographic display setups that may be corrupted by various hardware imperfections. Results tested on an unfiltered holographic display reveal that the proposed 3D-HoloNet can achieve 30 fps at full HD for one color channel using a consumer-level GPU while maintaining image quality comparable to iterative methods across multiple focused distances. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

引用

页码：1188 / 1191

页数：4