{"title":"Convolutional neural network optimisation to enhance ESPI fringe visibility","authors":"J. M. Crespo, V. Moreno","doi":"10.1051/epjconf/202226613007","DOIUrl":null,"url":null,"abstract":"The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI).","PeriodicalId":11731,"journal":{"name":"EPJ Web of Conferences","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EPJ Web of Conferences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/epjconf/202226613007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI).