High Performance CMOS Range Imaging
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This work is dedicated to CMOS based imaging with the emphasis on the noise modeling, characterization and optimization in order to contribute to the design of high performance imagers in general and range imagers in particular. CMOS is known to be superior to CCD due to its flexibility in terms of integration capabilities, but typically has to be enhanced to compete at parameters as for instance noise, dynamic range or spectral response. This work gathers the widespread theory on noise and extends the theory by a non-rigorous but potentially computing efficient algorithm to estimate noise in time sampled systems.…mehr

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Produktbeschreibung
This work is dedicated to CMOS based imaging with the emphasis on the noise modeling, characterization and optimization in order to contribute to the design of high performance imagers in general and range imagers in particular. CMOS is known to be superior to CCD due to its flexibility in terms of integration capabilities, but typically has to be enhanced to compete at parameters as for instance noise, dynamic range or spectral response. This work gathers the widespread theory on noise and extends the theory by a non-rigorous but potentially computing efficient algorithm to estimate noise in time sampled systems.
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Autorenporträt
Andreas Süss received his BSc from the University of Applied Sciences Düsseldorf in 2008 and a PhD degree from the University of Duisburg-Essen in 2014. From 2007 until 2014 he was affiliated to the Fraunhofer Institute IMS where he was working mainly on high-speed, low-noise imagers for e.g. ToF applications. From 2014 until 2015 he had a scholarship from the KU Leuven and worked as a postdoctoral researcher on global shutter imaging at the MICAS department in collaboration with IMEC, Leuven. As of 2015 he is hired as an R&D engineer in the IMEC imaging division, where he is currently responsible for the pixel development for global shutter and high-speed applications. His research interests include modeling, temporal noise, optimization, compressed sensing and depth imaging.