{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepcgh-3d-computer-generated-holography","title":"DeepCGH: 3D computer-generated holography using deep learning","arxiv_id":null,"date":"2020-08-31","proceeding":null,"authors":["M. Hossein Eybposh","NICHOLAS W. CAIRA","MATHEW ATISA","PRANEETH CHAKRAVARTHULA","NICOLAS C. PÉGARD"],"abstract":"The goal of computer-generated holography (CGH) is to synthesize custom illu-\r\nmination patterns by modulating a coherent light beam. CGH algorithms typically rely on\r\niterative optimization with a built-in trade-off between computation speed and hologram accuracy\r\nthat limits performance in advanced applications such as optogenetic photostimulation. We\r\nintroduce a non-iterative algorithm, DeepCGH, that relies on a convolutional neural network\r\nwith unsupervised learning to compute accurate holograms with fixed computational complexity.\r\nSimulations show that our method generates holograms orders of magnitude faster and with up to\r\n41% greater accuracy than alternate CGH techniques. Experiments in a holographic multiphoton\r\nmicroscope show that DeepCGH substantially enhances two-photon absorption and improves\r\nperformance in photostimulation tasks without requiring additional laser power.","url_abs":"https://www.osapublishing.org/DirectPDFAccess/813C5A4C-E63E-4D93-94773DED5E6D679E_437573/oe-28-18-26636.pdf?da=1&id=437573&seq=0&mobile=no","url_pdf":"https://www.osapublishing.org/DirectPDFAccess/813C5A4C-E63E-4D93-94773DED5E6D679E_437573/oe-28-18-26636.pdf?da=1&id=437573&seq=0&mobile=no","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepcgh-3d-computer-generated-holography","repo_url":"https://github.com/UNC-optics/DeepCGH","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}