{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/model-based-aberration-corrected-microscopy","title":"Model-based aberration corrected microscopy inside a glass tube","arxiv_id":"2311.13363","date":"2023-11-22","proceeding":null,"authors":["D. W. S. Cox","T. Knop","I. M. Vellekoop"],"abstract":"Microscope objectives achieve near diffraction-limited performance only when used under the conditions they are designed for. In non-standard geometries, such as thick cover slips or curved surfaces, severe aberrations arise, inevitably impairing high-resolution imaging. Correcting such large aberrations using standard adaptive optics can be challenging: existing solutions are either not suited for strong aberrations, or require extensive feedback measurements, consequently taking a significant portion of the photon budget. We demonstrate that it is possible to pre-compute the corrections needed for high-resolution imaging inside a glass tube based on a priori information only. Our ray-tracing based method achieved over an order of magnitude increase in image contrast without the need for a feedback signal.","url_abs":"https://arxiv.org/abs/2311.13363v1","url_pdf":"https://arxiv.org/pdf/2311.13363v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"model-based-aberration-corrected-microscopy","repo_url":"https://github.com/ivovellekoop/raylearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}