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 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.
Bildsensoren besitzen eine räumliche Inhomogenität, auch als Fixed Pattern Noise bekannt, das die Bildqualität herabsetzt. In diesem Paper wird eine bekannte Maximum-Likelihood-Methode 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.
Über die Autoren
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.
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.
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.
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