2-fold resolution increase and all-depth linearization using a neural network

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dc.contributor.authorMaliszewski, Krzysztof A.
dc.contributor.authorKolenderska, Sylwia M.
dc.date.accessioned2025-07-08T11:23:01Z
dc.date.issued2023-08-11
dc.description.abstractA neural network is proposed as a much better performing alternative to Fourier transformation. It processes raw OCT spectra into A-scans with twice better nominal axial resolution which remains intact at all depths even for an uncalibrated spectrometer and uncompensated chromatic dispersion.pl
dc.description.sponsorshipHorizon Europe, the European Union’s Framework Programme for Research and Innovation, SEQUOIA project, under Grant Agreement No. 101070062
dc.identifier.citationProc. of SPIE Vol. 12632pl
dc.identifier.otherhttps://doi.org/10.1117/12.2668845
dc.identifier.urihttps://repozytorium.umk.pl/handle/item/7219
dc.language.isoengpl
dc.publisherSPIEpl
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectNeural networkspl
dc.subjectImage processingpl
dc.subjectOptical coherence tomographypl
dc.subjectSpectral calibrationpl
dc.subjectImage resolutionpl
dc.subjectSpectroscopypl
dc.subjectSpectral resolutionpl
dc.title2-fold resolution increase and all-depth linearization using a neural networkpl
dc.typeinfo:eu-repo/semantics/conferenceObjectpl

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