Jump to ContentJump to Main Navigation
Show Summary Details
More options …

tm - Technisches Messen

Plattform für Methoden, Systeme und Anwendungen der Messtechnik

[TM - Technical Measurement: A Platform for Methods, Systems, and Applications of Measurement Technology
]

Editor-in-Chief: Puente León, Fernando / Zagar, Bernhard


IMPACT FACTOR 2018: 0.594

CiteScore 2018: 0.54

SCImago Journal Rank (SJR) 2018: 0.261
Source Normalized Impact per Paper (SNIP) 2018: 0.563

Online
ISSN
2196-7113
See all formats and pricing
More options …
Volume 80, Issue 10

Issues

PRNU and DSNU Maximum Likelihood Estimation Using Sensor Statistics

PRNU- und DSNU-Maximum-Likelihood-Schätzung mit Hilfe der Sensorstatistik

Marc Geese / Paul Ruhnau / Bernd Jähne
Published Online: 2013-10-29 | DOI: https://doi.org/10.1515/teme.2013.0039

Abstract

Image sensors come with a spatial inhomogeneity, known as Fixed Pattern Noise, that degrades the image quality. In this paper a known maximum likelihood estimation method [1] is extended in a way that it allows to estimate the two parameters DSNU and PRNU of a sensor's fixed pattern noise. The method's input are the averaged sensor responses and the corresponding pairwise sensor covariances. First results show a significant performance increase compared to related methods.

Zusammenfassung

Bildsensoren besitzen eine räumliche Inhomogenität, auch als Fixed Pattern Noise bekannt, das die Bildqualität herabsetzt. In diesem Paper wird eine bekannte Maximum-Likelihood-Methode [1] erweitert, so dass eine kombinierte Schätzung der beiden Parameter DSNU und PRNU des Fixed Pattern Noise möglich ist. Die neue Methode benutzt die gemittelten Sensor-Antworten und die dazugehörigen paarweisen Sensor-Kovarianzen. Erste Ergebnisse zeigen eine signifikante Performancesteigerung gegenüber vergleichbaren Methoden.

Keywords: Fixed pattern noise; DSNU; PRNU; maximum likelihood estimation

Schlagwörter: Fixed Pattern Noise; DSNU; PRNU; Maximum-Likelihood-Schätzung

About the article

Marc Geese

Dipl.-Phys. Marc Geese studied Physics and received his Diploma in 2008 at the University of Frankfurt in the field of image processing with cellular neural networks. He continued his studies at the University of Manchester in electrical and electronic engineering of vision chips and received a Master of Philosophy in 2009. The same year he entered the PhD program of the Robert Bosch GmbH in the field of video-based driver assistance systems. The current research is conducted in close collaboration with the Heidelberg Collaboratory for Image Processing (HCI) at the University of Heidelberg and is supervised by Prof. Bernd Jähne. His research interests concentrate on the calibration of image sensors, especially for the field of video-based driver assistance systems.

Robert Bosch GmbH, Daimlerstraße 6, 71229 Leonberg, Germany

Paul Ruhnau

Dr. Paul Ruhnau studied Computer Science and received his Diploma and Doctoral degree from Mannheim University in 2003 and 2007, respectively. Since 2006, he works at Robert-Bosch GmbH in Leonberg as a developer for computer vision algorithms. His research interests concentrate on algorithm development for video-based driver assistance systems and image sensors.

Robert Bosch GmbH, Daimlerstraße 6, 71229 Leonberg, Germany

Bernd Jähne

Prof. Dr. Bernd Jähne studied Physics and received his Diploma, Doctoral degree and Habilitation degree from Heidelberg University in 1977, 1980, and 1985, respectively, and a Habilitation degree in Applied Computer Science from the University of Hamburg-Harburg in 1992. From 1988 to 2000 he held a research professorship at the Scripps Institution of Oceanography, University of California in San Diego. Since 1994 he has been a Professor of Physics at the Interdisciplinary Center of Scientific Computing (IWR) of Heidelberg University. In 2008 he became coordinating director of the Heidelberg Collaboratory for Image Processing (HCI), an Industry on Campus Institution of Heidelberg University with the participation of several companies. Since 2008, he has also been deputy managing director of the IWR. His research interests include small-scale air-sea interaction, imaging systems, especially time-of-flight imaging, computational photography, foundations of image and image sequence processing, and the application of image processing techniques in science and industry.

Heidelberg Collaboratory for Image Processing, Speyerer Straße 6, 69115 Heidelberg, Germany


Received: 2013-06-26

Published Online: 2013-10-29

Published in Print: 2013-10-01


Citation Information: tm – Technisches Messen tm - Technisches Messen, Volume 80, Issue 10, Pages 321–328, ISSN (Online) 2196-7113, ISSN (Print) 0171-8096, DOI: https://doi.org/10.1515/teme.2013.0039.

Export Citation

© 2013 by Walter de Gruyter Berlin Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in