4K-DMDNet:diffraction model-driven network for 4K computer-generated holography  

作　　者：Kexuan Liu Jiachen Wu Zehao He Liangcai Cao 

机构地区：[1]State Key Laboratory of Precision Measurement Technology and Instruments,Department of Precision Instruments,Tsinghua University,Beijing 100084,China

出　　处：《Opto-Electronic Advances》2023年第5期17-29,共13页光电进展（英文）

基　　金：We are grateful for financial supports from National Natural Science Foundation of China(62035003,61775117);China Postdoctoral Science Foundation(BX2021140);Tsinghua University Initiative Scientific Research Program(20193080075).

摘　　要：Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography(CGH).Current data-driven deep learning algorithms face the challenge that the labeled training datasets limit the training performance and generalization.The model-driven deep learning introduces the diffraction model into the neural network.It eliminates the need for the labeled training dataset and has been extensively applied to hologram generation.However,the existing model-driven deep learning algorithms face the problem of insufficient constraints.In this study,we propose a model-driven neural network capable of high-fidelity 4K computer-generated hologram generation,called 4K Diffraction Model-driven Network(4K-DMDNet).The constraint of the reconstructed images in the frequency domain is strengthened.And a network structure that combines the residual method and sub-pixel convolution method is built,which effectively enhances the fitting ability of the network for inverse problems.The generalization of the 4K-DMDNet is demonstrated with binary,grayscale and 3D images.High-quality full-color optical reconstructions of the 4K holograms have been achieved at the wavelengths of 450 nm,520 nm,and 638 nm.

关 键 词：computer-generated holography deep learning model-driven neural network sub-pixel convolution OVERSAMPLING 

分 类 号：O438.1[机械工程—光学工程] TP391.41[理学—光学]