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Lett."],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:p>Lensless imaging is attractive for its compact and low-cost design. However, high-accuracy and robust phase retrieval from a single pattern remains challenging and critically relies on accurate priors. Here, we propose a parallel consensus optimization termed PaCO to achieve robust and high-accuracy lensless imaging. PaCO decomposes the ill-posed problem into parallel subproblems with a distributed computational architecture. Each subproblem is handled by dedicated solvers that respectively enforce data fidelity, Fourier support, and spatial smoothness. The local solutions are aggregated into a consensus estimate. Experiments on a mask-based system demonstrate that PaCO can reconstruct objects without precise support or distance priors and accurately retrieve vortex and binary-phase-plate wavefronts, while conventional methods suffer from reconstruction instability. Statistical tests confirm that PaCO improves the success rate from 74% to 96.8% while halving the processing time. The parallel architecture of PaCO is inherently CUDA-friendly, holding strong potential for real-time processing in extreme-scale imaging workflows.<\/jats:p>","DOI":"10.1364\/ol.597012","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T13:00:10Z","timestamp":1780923610000},"page":"3777","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parallel consensus optimization for high-accuracy and robust lensless imaging"],"prefix":"10.1364","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6075-2954","authenticated-orcid":true,"given":"Ying","family":"Li","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2491-5673","authenticated-orcid":true,"given":"Yunhui","family":"Gao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-7364","authenticated-orcid":true,"given":"Shuhe","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8099-2948","authenticated-orcid":true,"given":"Liangcai","family":"Cao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03cve4549","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"ol-51-13-3777-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"}]}}