{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:09:43Z","timestamp":1785409783734,"version":"3.56.0"},"reference-count":29,"publisher":"Optica Publishing Group","issue":"8","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People\u2019s Republic of China","doi-asserted-by":"crossref","award":["2022YFF0706300"],"award-info":[{"award-number":["2022YFF0706300"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/027s68j25","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["J. Opt. Soc. Am. A"],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:p>Coherence scanning interferometry (CSI) enables non-contact surface measurement with high axial resolution, but its accuracy is degraded in practical complex-surface metrology by two coupled effects: vibration-induced non-uniform axial sampling and diffraction-induced slope\/edge errors. Axial-position sensing can provide frame-wise scanning positions, but it does not correct the optical-transfer-function degradation caused by finite-aperture imaging. Conversely, conventional inverse filtering compensates for diffraction-related attenuation but usually assumes uniform axial sampling. This paper proposes a unified variational reconstruction framework that incorporates measured non-uniform axial positions into an NUFFT-based CSI forward model with sparse regularization. The proposed method jointly models non-uniform sampling and optical-transfer-function degradation, and is evaluated against uniform-sampling inverse filtering, non-uniform least-squares phase recovery, and a two-step adjoint-NUFFT\/IF baseline. Numerical simulations demonstrate improved robustness under strong sampling non-uniformity, residual axial-position errors, detector noise, and varying spectral widths. For the tested step sample, the proposed method achieves a step-height error of 0.052\u00a0nm and suppresses directional batwing artifacts to approximately the nanometer level. GPU implementation on an NVIDIA RTX 5070 Ti processes a representative 400\u00d7400\u00d7500 volume in 8.929\u00a0s, with only about 12.5% overhead relative to the two-step adjoint-NUFFT\/IF baseline. These results indicate that the proposed framework is promising for calibrated CSI systems with available frame-wise axial positions.<\/jats:p>","DOI":"10.1364\/josaa.601571","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:00:10Z","timestamp":1781542810000},"page":"1176","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unified variational framework for simultaneous correction of non-uniform sampling and diffraction-induced slope errors in coherence scanning interferometry"],"prefix":"10.1364","volume":"43","author":[{"given":"Yulan","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0419fj215","id-type":"ROR","asserted-by":"publisher"}]},{"name":"University of Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0220-769X","authenticated-orcid":true,"given":"Anqi","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0419fj215","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwei","family":"Hao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0419fj215","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"He","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0419fj215","id-type":"ROR","asserted-by":"publisher"}]},{"name":"University of Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxi","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/0419fj215","id-type":"ROR","asserted-by":"publisher"}]},{"name":"University of Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"josaa-43-8-1176-R1","doi-asserted-by":"publisher","first-page":"110264","DOI":"10.1016\/j.optlastec.2023.110264","type":"journal-article","volume":"170","author":"Xin","year":"2024","journal-title":"Opt. 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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"}]}}