{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T21:58:56Z","timestamp":1772575136853,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:00:00Z","timestamp":1724544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic and Applied Basic Research Program of Guangdong Province","award":["2020A1515110523"],"award-info":[{"award-number":["2020A1515110523"]}]},{"name":"Basic and Applied Basic Research Program of Guangdong Province","award":["2024JC-YBQN-0626"],"award-info":[{"award-number":["2024JC-YBQN-0626"]}]},{"name":"Basic and Applied Basic Research Program of Guangdong Province","award":["QTZX22079"],"award-info":[{"award-number":["QTZX22079"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2020A1515110523"],"award-info":[{"award-number":["2020A1515110523"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2024JC-YBQN-0626"],"award-info":[{"award-number":["2024JC-YBQN-0626"]}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["QTZX22079"],"award-info":[{"award-number":["QTZX22079"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020A1515110523"],"award-info":[{"award-number":["2020A1515110523"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2024JC-YBQN-0626"],"award-info":[{"award-number":["2024JC-YBQN-0626"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["QTZX22079"],"award-info":[{"award-number":["QTZX22079"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional methods of hologram generation, such as point-, polygon-, and layer-based physical simulation approaches, suffer from substantial computational overhead and generate low-fidelity holograms. Deep learning-based computer-generated holography demonstrates effective performance in terms of speed and hologram fidelity. There is potential to enhance the network\u2019s capacity for fitting and modeling in the context of computer-generated holography utilizing deep learning methods. Specifically, the ability of the proposed network to simulate Fresnel diffraction based on the provided hologram dataset requires further improvement to meet expectations for high-fidelity holograms. We propose a neural architecture called Holo-U2Net to address the challenge of generating a high-fidelity hologram within an acceptable time frame. Holo-U2Net shows notable performance in hologram evaluation metrics, including an average structural similarity of 0.9988, an average peak signal-to-noise ratio of 46.75 dB, an enhanced correlation coefficient of 0.9996, and a learned perceptual image patch similarity of 0.0008 on the MIT-CGH-4K large-scale hologram dataset.<\/jats:p>","DOI":"10.3390\/s24175505","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:32:01Z","timestamp":1724643121000},"page":"5505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Holo-U2Net for High-Fidelity 3D Hologram Generation"],"prefix":"10.3390","volume":"24","author":[{"given":"Tian","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, South Taibai Road No. 2, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Vision, Xidian University, South Taibai Road No. 2, Xi\u2019an 710071, China"},{"name":"Guangzhou Institute of Technology, Xidian University, Zhimin Road No. 83, Guangzhou 510555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2743-2017","authenticated-orcid":false,"given":"Zixiang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, South Taibai Road No. 2, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Vision, Xidian University, South Taibai Road No. 2, Xi\u2019an 710071, China"},{"name":"Guangzhou Institute of Technology, Xidian University, Zhimin Road No. 83, Guangzhou 510555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1038\/s42256-023-00704-7","article-title":"Self-supervised learning of hologram reconstruction using physics consistency","volume":"5","author":"Luzhe","year":"2023","journal-title":"Nat. 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