{"title":"Two-fold resolution increase and all-depth linearization using a neural network","authors":"Krzysztof A. Maliszewski, Sylwia M. Kolenderska","doi":"10.1117/12.2668845","DOIUrl":null,"url":null,"abstract":"A 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.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Conference on Biomedical Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2668845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A 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.