Date & Time:
Wednesday, June 15, 2011; 12:00 PM
Before the advent of digital image sensors, photography, for the most part of its history, used film to record light information. In this talk, I will present a new digital image sensor that is reminiscent of photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity.
To analyze its performance, we formulate the binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cramer-Rao lower bound of the estimation variance approaches that of an ideal unquantized sensor, that is, as if there were no quantization in the sensor measurements. Furthermore, this theoretical performance bound is shown to be asymptotically achievable by practical image reconstruction algorithms based on maximum likelihood estimators.
Numerical results on both synthetic data and images taken by a prototype sensor verify the theoretical analysis and the effectiveness of the proposed image reconstruction algorithm. They also demonstrate the benefit of using the new binary sensor in applications involving high dynamic range imaging.
Joint work with Feng Yang, Luciano Sbaiz and Martin Vetterli.
Dr. Yue M. Lu
Harvard School of Engineering and Applied Sciences
Yue M. Lu received his M.Sc. degree in mathematics and Ph.D. degree in electrical engineering, both in 2007, from the University of Illinois at Urbana-Champaign. He was a Research Assistant at the University of Illinois at Urbana-Champaign, and has worked for Microsoft Research Asia, Beijing, China and Siemens Corporate Research, Princeton, NJ. In 2007, He joined the Audiovisual Communications Laboratory at Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland, where he was a postdoctoral researcher and lecturer. Since 2010, he has been an Assistant Professor of Electrical Engineering at Harvard University, Cambridge, MA. His research interests include spatiotemporal sampling of dynamic processes, sparse signal processing, and computational imaging.
He received the Most Innovative Paper Award of IEEE International Conference on Image Processing (ICIP) in 2006, the Student Paper Award of IEEE ICIP in 2007. He also co-authored a paper (with Ivan Dokmanic and Martin Vetterli) that won the Best Student Paper Award of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2011.