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Lett."],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:p>Single-frame hologram reconstruction without training remains challenging for real-world samples due to the difficulty of modeling coupled amplitude and phase information. To address this issue, we develop a fully complex-domain attention mechanism that reformulates attention computation directly in the complex field using complex inner products, enabling more effective representation of amplitude-phase coupling. Based on this formulation, a general fully complex-domain transformer, termed HoloFormer v3, is constructed and instantiated within an untrained reconstruction framework. An auxiliary free-space back-propagation initialization reformulates the highly nonlinear hologram-to-object complex amplitude inversion as a twin-image-suppressed optimization in the object plane. Experiments on real samples demonstrate improved reconstruction accuracy and faster convergence compared with existing untrained methods.<\/jats:p>","DOI":"10.1364\/ol.589365","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T12:00:09Z","timestamp":1772712009000},"page":"1776","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Untrained physics-enhanced fully complex transformer for single-frame hologram reconstruction"],"prefix":"10.1364","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8870-4356","authenticated-orcid":true,"given":"Ziqi","family":"Bai","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianming","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2598-9527","authenticated-orcid":true,"given":"Cheng","family":"Guo","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junjun","family":"Jiang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01yqg2h08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7333-9975","authenticated-orcid":true,"given":"Xiangyang","family":"Ji","sequence":"additional","affiliation":[{"name":"Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"ol-51-7-1776-R1","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1038\/s41377-024-01453-x","type":"journal-article","volume":"13","author":"Huang","year":"2024","journal-title":"Light. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}