Publisher Correction: Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2045-2322
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Publisher Correction: Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution ; volume:8 ; number:1 ; day:5 ; month:7 ; year:2018 ; pages:1 ; date:12.2018
Scientific reports ; 8, Heft 1 (5.7.2018), 1, 12.2018
- Urheber
-
Wang, Zhouping
- Beteiligte Personen und Organisationen
-
Jin, Xin
Dai, Qionghai
SpringerLink (Online service)
- DOI
-
10.1038/s41598-018-28652-y
- URN
-
urn:nbn:de:101:1-2018091214150410003552
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:39 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Wang, Zhouping
- Jin, Xin
- Dai, Qionghai
- SpringerLink (Online service)
Ähnliche Objekte (12)
Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution
Demographic Transition and Population Development in Belarus: Theory and Reality
The role of effort in understanding academic achievements: empirical evidence from China
Predicting academic success: machine learning analysis of student, parental, and school efforts
Macroeconomics with a Thick Pen
Misdiagnosis and Treatment of Corneal Complications Caused by Suture Exposure After Buried-Suture Double-Eyelid Blepharoplasty
Strong convergent result for quasi-nonexpansive mappings in Hilbert spaces
Disturbance rejection model predictive control of lower limb rehabilitation exoskeleton
Improving Topological Confinement Using Asymmetric Elements
Government governance, executive networks and enterprise R&D expenditure
Does the choice of realized covariance measures empirically matter? A Bayesian density prediction approach
Nanoscale switches for memorizing polymers
Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution
Demographic Transition and Population Development in Belarus: Theory and Reality
The role of effort in understanding academic achievements: empirical evidence from China
Predicting academic success: machine learning analysis of student, parental, and school efforts
Macroeconomics with a Thick Pen
Misdiagnosis and Treatment of Corneal Complications Caused by Suture Exposure After Buried-Suture Double-Eyelid Blepharoplasty
Strong convergent result for quasi-nonexpansive mappings in Hilbert spaces
Disturbance rejection model predictive control of lower limb rehabilitation exoskeleton
Improving Topological Confinement Using Asymmetric Elements
Government governance, executive networks and enterprise R&D expenditure
Does the choice of realized covariance measures empirically matter? A Bayesian density prediction approach
Nanoscale switches for memorizing polymers
Non-invasive imaging through strongly scattering media based on speckle pattern estimation and deconvolution
Demographic Transition and Population Development in Belarus: Theory and Reality
The role of effort in understanding academic achievements: empirical evidence from China
Predicting academic success: machine learning analysis of student, parental, and school efforts
Macroeconomics with a Thick Pen
Misdiagnosis and Treatment of Corneal Complications Caused by Suture Exposure After Buried-Suture Double-Eyelid Blepharoplasty
Strong convergent result for quasi-nonexpansive mappings in Hilbert spaces
Disturbance rejection model predictive control of lower limb rehabilitation exoskeleton
Improving Topological Confinement Using Asymmetric Elements
Government governance, executive networks and enterprise R&D expenditure
Does the choice of realized covariance measures empirically matter? A Bayesian density prediction approach