{"title":"卷积神经网络优化，提高ESPI条纹可见性","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":"{\"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}","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}
Convolutional neural network optimisation to enhance ESPI fringe visibility
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).
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