{"title":"两倍的分辨率增加和全深度线性化使用神经网络","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":"{\"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}","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}
Two-fold resolution increase and all-depth linearization using a neural network
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.