{"title":"4K- dmdnet:用于4K计算机生成全息的衍射模型驱动网络","authors":"Kexuan Liu, Jiachen Wu, Zehao He, Liang Cao","doi":"10.29026/oea.2023.220135","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":19611,"journal":{"name":"Opto-Electronic Advances","volume":"1 1","pages":""},"PeriodicalIF":15.3000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"4K-DMDNet: diffraction model-driven network for 4K computer-generated holography\",\"authors\":\"Kexuan Liu, Jiachen Wu, Zehao He, Liang Cao\",\"doi\":\"10.29026/oea.2023.220135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":19611,\"journal\":{\"name\":\"Opto-Electronic Advances\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":15.3000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Opto-Electronic Advances\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.29026/oea.2023.220135\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Opto-Electronic Advances","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.29026/oea.2023.220135","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
4K-DMDNet: diffraction model-driven network for 4K computer-generated holography
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.
期刊介绍:
Opto-Electronic Advances (OEA) is a distinguished scientific journal that has made significant strides since its inception in March 2018. Here's a collated summary of its key features and accomplishments:
Impact Factor and Ranking: OEA boasts an impressive Impact Factor of 14.1, which positions it within the Q1 quartiles of the Optics category. This high ranking indicates that the journal is among the top 25% of its field in terms of citation impact.
Open Access and Peer Review: As an open access journal, OEA ensures that research findings are freely available to the global scientific community, promoting wider dissemination and collaboration. It upholds rigorous academic standards through a peer review process, ensuring the quality and integrity of the published research.
Database Indexing: OEA's content is indexed in several prestigious databases, including the Science Citation Index (SCI), Engineering Index (EI), Scopus, Chemical Abstracts (CA), and the Index to Chinese Periodical Articles (ICI). This broad indexing facilitates easy access to the journal's articles by researchers worldwide.