In this talk, I will briefly present the various activities in the Image and Video Processing Laboratory I am directing, in the EECS Department at Northwestern University. I will then focus on the presentation of some of our recent results in solving inverse problems, such as, the image recovery, super-resolution, and compressive sensing problems. I will present both analytical and learning approaches for solving such problems. More specifically, I will first present hierarchical Bayesian approaches for blind deconvolution and image super-resolution. I will then present dictionary approaches for solving the video super-resolution problem as well as the problem of fusing visible and X-ray fluorescence images. Finally, I will present some of our results using deep neural networks for temporal compressive sampling. I will conclude the talk by discussing the impact of learning approaches in solving inverse problems.

Bio

Aggelos K. Katsaggelos received the Diploma degree in electrical and mechanical engineering from the Aristotelian University of Thessaloniki, Greece, in 1979, and the M.S. and Ph.D. degrees in Electrical Engineering from the Georgia Institute of Technology, in 1981 and 1985, respectively.
 
In 1985, he joined the Department of Electrical Engineering and Computer Science at Northwestern University, where he is currently a Professor holder of the Joseph Cummings chair.  He was previously the holder of the Ameritech Chair of Information Technology and the AT&T Research Chair. He is also a member of the Academic Staff, NorthShore University Health System and an affiliated faculty at the Department of Linguistics.
 
He has published extensively in the areas of multimedia signal processing and communications and machine learning (over 250 journal papers, 500 conference papers and 40 book chapters) and is the holder of 25 international patents. He is the co-author of Rate-Distortion Based Video Compression (Kluwer, 1997), Super-Resolution for Images and Video (Claypool, 2007), Joint Source-Channel Video Transmission (Claypool, 2007), and Machine Learning Refined (Cambridge University Press, 2016).  He has supervised 56 Ph.D. theses so far. He has been teaching the popular course on Coursera “Fundamentals of Digital Image and Video Processing.”
 
Among his many professional activities Prof. Katsaggelos was Editor-in-Chief of the IEEE Signal Processing Magazine (1997–2002), a BOG Member of the IEEE Signal Processing Society (1999–2001), a member of the Publication Board of the IEEE Proceedings (2003-2007), and he is currently a Member of the Award Board of the IEEE Signal Processing Society. He is a Fellow of the IEEE (1998) and SPIE (2009) and the recipient of the IEEE Third Millennium Medal (2000), the IEEE Signal Processing Society Meritorious Service Award (2001), the IEEE Signal Processing Society Technical Achievement Award (2010), an IEEE Signal Processing Society Best Paper Award (2001), an IEEE ICME Paper Award (2006), an IEEE ICIP Paper Award (2007), an ISPA Paper Award (2009), and a EUSIPCO paper award (2013). He was a Distinguished Lecturer of the IEEE Signal Processing Society (2007–2008).