Recovery from Linear Measurements via Denoising and Approximate Message Passing

Start: 02/04/2015 - 1:15pm
End  : 02/04/2015 - 2:15pm

Applied Math Seminar

Dror Baron (NC State)



Approximate message passing (AMP) decouples linear inverse problems into iterative additive white 
Gaussian noise (AWGN) scalar channel denoising problems. The first part of the talk provides a 
tutorial-style overview of AMP, including advantages, a convenient design methodology, and 
possible pitfalls. The second part describes how we have designed denoising algorithms that 
recover signals from AWGN, and applied these denoisers within AMP to reconstruct signals from 
linear mixing channels. Examples include parametric denoising for parametric signals, universal 
denoising for stationary ergodic signals, and image denoising for natural images. Our favorable 
numerical results indicate that AMP is a promising tool for solving linear inverse problems.
Dror Baron received the B.Sc. (summa cum laude) and M.Sc. degrees from
the Technion - Israel Institute of Technology, Haifa, Israel, in 1997
and 1999, and the Ph.D. degree from the University of Illinois at
Urbana-Champaign in 2003, all in electrical engineering.
From 1997 to 1999, he worked at Witcom Ltd. in modem design. From 1999
to 2003, he was a research assistant at the University of Illinois at
Urbana-Champaign, where he was also a Visiting Assistant Professor in
2003. From 2003 to 2006, he was a Postdoctoral Research Associate in
the Department of Electrical and Computer Engineering at Rice
University, Houston, TX. From 2007 to 2008, he was a quantitative
financial analyst with Menta Capital, San Francisco, CA. From 2008 to
2010 he was a visiting scientist in the Department of Electrical
Engineering at the Technion. Dr. Baron joined the Department of
Electrical and Computer Engineering at North Carolina State University
in 2010 as an assistant professor. His research interests include
information theory and statistical signal processing.



Kravis Center 166