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Opt."],"published-print":{"date-parts":[[2026,10,10]]},"abstract":"<jats:p>Displacement is an imperative metric for assessing structural performance. However, the accuracy of structural displacement measurement is constrained by the physical resolution of the camera, particularly in long-range monitoring scenarios. To address these constraints, a cost-effective long-range structural displacement measurement system based on a two-stage method integrating super-resolution reconstruction and sub-pixel registration (SRSR) is proposed in this study. Initially, the proposed method achieves texture detail enhancement of low-resolution images through super-resolution reconstruction, thus improving the accuracy of visual tracking. Subsequently, a continuous convolution operator based on natural texture on the structural surface is utilized to achieve accurate sub-pixel registration. Finally, the performance of the proposed methodology is verified with field tests on a scaled truss bridge and a cable-stayed bridge. The displacement measurement accuracy is significantly enhanced through the implementation of a two-stage technique based on SRSR (including steps I and II), in comparison to the utilization of the sub-pixel registration (step II only) in isolation. The cost-effective vision-based system developed in this study provides an alternative for long-range structural displacement measurement.<\/jats:p>","DOI":"10.1364\/ao.606610","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T13:00:10Z","timestamp":1789131610000},"page":"9987","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cost-effective long-range measurement of structural displacement using a two-stage method integrating super-resolution reconstruction and sub-pixel registration"],"prefix":"10.1364","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6307-7123","authenticated-orcid":true,"given":"Chi","family":"Zhang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03je71k37","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03je71k37","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Dai","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03je71k37","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaosong","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03je71k37","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ao-65-29-9987-R1","doi-asserted-by":"publisher","first-page":"5115","DOI":"10.1364\/AO.491428","type":"journal-article","volume":"62","author":"Pan","year":"2023","journal-title":"Appl. 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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"}]}}