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

查看全文 作　　者：Kexuan [1]Liu;Jiachen [1]Wu;Zehao [1]He;Liangcai [1]Cao 高影响力作者 机构地区：[1]State Key Laboratory of Precision Measurement Technology and Instruments,Department of Precision Instruments,Tsinghua University,Beijing 100084,China高影响力机构 出　　处：《Opto-Electronic Advances》索引2023年第6卷第5期,共13页高影响力期刊 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
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