Modelling a Microscope as Low Dimensional Subspace of Operators

Valentin Debarnot, Paul Escande, T. Mangeat, P. Weiss
{"title":"Modelling a Microscope as Low Dimensional Subspace of Operators","authors":"Valentin Debarnot, Paul Escande, T. Mangeat, P. Weiss","doi":"10.23919/Eusipco47968.2020.9287603","DOIUrl":null,"url":null,"abstract":"We propose a novel approach to calibrate a microscope. Instead of seeking a single linear integral operator (e.g. a convolution with a point spread function) that describes its action, we propose to describe it as a low-dimensional linear subspace of operators. By doing so, we are able to capture its variations with respect to multiple factors such as changes of temperatures and refraction indexes, tilts of optical elements or different states of spatial light modulator. While richer than usual, this description however suffers from a serious limitation: it cannot be used directly to solve the typical inverse problems arising in computational imaging. As a second contribution, we therefore design an original algorithm to identify the operator from the image of a few isolated spikes. This can be achieved experimentally by adding fluorescent micro-beads around the sample. We demonstrate the potential of the approach on a challenging deblurring problem.Important note: this paper is an abridged version of a preprint [3] by the same authors, submitted for a journal publication.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"209 1","pages":"765-769"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 28th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/Eusipco47968.2020.9287603","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

We propose a novel approach to calibrate a microscope. Instead of seeking a single linear integral operator (e.g. a convolution with a point spread function) that describes its action, we propose to describe it as a low-dimensional linear subspace of operators. By doing so, we are able to capture its variations with respect to multiple factors such as changes of temperatures and refraction indexes, tilts of optical elements or different states of spatial light modulator. While richer than usual, this description however suffers from a serious limitation: it cannot be used directly to solve the typical inverse problems arising in computational imaging. As a second contribution, we therefore design an original algorithm to identify the operator from the image of a few isolated spikes. This can be achieved experimentally by adding fluorescent micro-beads around the sample. We demonstrate the potential of the approach on a challenging deblurring problem.Important note: this paper is an abridged version of a preprint [3] by the same authors, submitted for a journal publication.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
显微镜作为算子的低维子空间的建模
我们提出了一种校准显微镜的新方法。代替寻找描述其作用的单一线性积分算子(例如与点扩展函数的卷积),我们建议将其描述为算子的低维线性子空间。通过这样做,我们能够捕捉到它在多种因素方面的变化,如温度和折射率的变化,光学元件的倾斜或空间光调制器的不同状态。虽然这种描述比通常更丰富,但它有一个严重的局限性:它不能直接用于解决计算成像中出现的典型逆问题。作为第二个贡献,我们因此设计了一个原始算法,从几个孤立的尖峰图像中识别算子。这可以通过在样品周围添加荧光微珠来实现实验。我们展示了该方法在一个具有挑战性的去模糊问题上的潜力。重要提示:这篇论文是同一作者的预印本[3]的删节版,提交给期刊发表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Eusipco 2021 Cover Page A graph-theoretic sensor-selection scheme for covariance-based Motor Imagery (MI) decoding Hidden Markov Model Based Data-driven Calibration of Non-dispersive Infrared Gas Sensor Deep Transform Learning for Multi-Sensor Fusion Two Stages Parallel LMS Structure: A Pipelined Hardware Architecture
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1