{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:46:30Z","timestamp":1792788390099,"version":"4.5.0"},"reference-count":40,"publisher":"Optica Publishing Group","issue":"4","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"name":"Natural Science Foundation of\n\t\t\t\t\t\tShanghai","award":["25ZR1402180"],"award-info":[{"award-number":["25ZR1402180"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of\n\t\t\t\t\t\tChina","doi-asserted-by":"publisher","award":["62201338"],"award-info":[{"award-number":["62201338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Appl. Opt."],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:p>Digital holography is one of the key technologies for acquiring\n\t\t\t\t\twavefront information of three-dimensional objects, and obtaining\n\t\t\t\t\thigh-quality holograms is a prerequisite. To address the issue of\n\t\t\t\t\tlow-resolution holograms caused by pixel size limitations and\n\t\t\t\t\tdiffraction effects in imaging sensors (CCD\/CMOS), the common approach\n\t\t\t\t\tinvolves using large datasets for super-resolution reconstruction\n\t\t\t\t\tthrough deep-learning methods. Due to hardware limitations and\n\t\t\t\t\tconstraints in the experimental environment, it is more difficult to\n\t\t\t\t\tcreate large datasets. To address this challenge, this study proposes\n\t\t\t\t\ta small-sample super-resolution reconstruction framework based on\n\t\t\t\t\tholographic super-resolution CFAT (HSR-CFAT), for the first time, to\n\t\t\t\t\tthe best of our knowledge. The HSR-CFAT model uses a residual hybrid\n\t\t\t\t\tattention group (RHAG) to fuse the hybrid attention block dense\n\t\t\t\t\t(HAB_D), hybrid attention block sparse (HAB_S), and overlapping\n\t\t\t\t\tcross-attention block (OCAB). Together with sliding window attention\n\t\t\t\t\t(SWA) and dynamic-resolution processing, it efficiently extracts\n\t\t\t\t\tmulti-scale features to generate high-precision computer-generated\n\t\t\t\t\tholograms (CGHs). Further optimization of computational efficiency and\n\t\t\t\t\tphase accuracy through the phase-aware upsampling (PAU) module,\n\t\t\t\t\tachieving robust pixel-level reconstruction and overcoming the\n\t\t\t\t\treliance of traditional methods on large datasets. A small-scale\n\t\t\t\t\tdataset was constructed by collecting 377 multi-view holograms.\n\t\t\t\t\tSuper-resolution processing was performed directly on the holograms to\n\t\t\t\t\tovercome sensor pixel limitations, and the phase reconstruction module\n\t\t\t\t\twas fused to generate high-resolution phase images. The experimental\n\t\t\t\t\tresults demonstrate that the network can process the collected\n\t\t\t\t\tmulti-scale holographic images effectively, achieving significantly\n\t\t\t\t\tbetter performance than existing methods in terms of both subjective\n\t\t\t\t\tvisual quality and objective evaluation metrics. This opens up a new\n\t\t\t\t\ttechnical pathway for super-resolution reconstruction in off-axis\n\t\t\t\t\tdigital holographic imaging.<\/jats:p>","DOI":"10.1364\/ao.582870","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T18:00:09Z","timestamp":1766512809000},"page":"1021","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HSR-CFAT-based small-sample super-resolution reconstruction framework for off-axis digital holography"],"prefix":"10.1364","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5374-3514","authenticated-orcid":true,"given":"Siyu","family":"Wei","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0557b9y08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0557b9y08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianci","family":"Zhao","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1916-709X","authenticated-orcid":true,"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Shanghai for Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzirui","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0557b9y08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-0418","authenticated-orcid":true,"given":"Xin","family":"Tang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0557b9y08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Kong","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0557b9y08","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"ao-65-4-1021-R1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781139061346","volume-title":"Introduction to Modern Digital\n\t\t\t\t\tHolography: With Matlab","author":"Poon","year":"2014"},{"key":"ao-65-4-1021-R2","doi-asserted-by":"publisher","first-page":"143","DOI":"10.3390\/app8010143","type":"journal-article","volume":"8","author":"Liu","year":"2018","journal-title":"Appl.\n\t\t\t\t\tSci."},{"key":"ao-65-4-1021-R3","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1364\/OL.42.001720","type":"journal-article","volume":"42","author":"Ren","year":"2017","journal-title":"Opt. 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All rights, including\n\t\t\t\t\tfor text and data mining (TDM), Artificial Intelligence (AI) training,\n\t\t\t\t\tand similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}