Communications and Signal Processing Seminar

Inverse Problem Regularization Using Sparsity Models

Jeff FesslerProfessorUniversity of Michigan, Department of EECS

Inverse problems are usually ill-conditioned or ill-posed, meaning that there are multiple candidate solutions that all fit the measured data equally or reasonably well. Some type of prior information or modeling assumptions are needed to distinguish among candidate solutions. This talk will summarize a variety of classical and contemporary methods for regularizing inverse problems, emphasizing methods based on sparse signal models. A recent dictionary learning method that is more compute efficient than K-SVD will be highlighted as a regularizer, and used to perform sparse-view tomographic image reconstruction. The presentation will have equal parts of colorful pictures and mathematical formulas and be aimed a level suitable for students in EECS 556. Joint work with Sai Ravishankar and Raj Nadakuditi
Jeff Fessler received the BSEE degree from Purdue University in 1985, the MSEE degree from Stanford University in 1986, and the M.S. degree in Statistics from Stanford University in 1989. From 1985 to 1988 he was a National Science Foundation Graduate Fellow at Stanford, where he earned a Ph.D. in electrical engineering in 1990. He has worked at the University of Michigan since then. From 1991 to 1992 he was a Department of Energy Alexander Hollaender Post-Doctoral Fellow in the Division of Nuclear Medicine. From 1993 to 1995 he was an Assistant Professor in Nuclear Medicine and the Bioengineering Program. He is now a Professor in the Departments of Electrical Engineering and Computer Science, Radiology, and Biomedical Engineering. He became a Fellow of the IEEE in 2006, for contributions to the theory and practice of image reconstruction. He received the Francois Erbsmann award for his IPMI93 presentation, and the Edward Hoffman Medical Imaging Scientist Award in 2013. He has served as an associate editor for IEEE Transactions on Medical Imaging, the IEEE Signal Processing Letters, and the IEEE Transactions on Image Processing, and is currently serving as an associate editor for the IEEE Transactions on Computational Imaging. He has chaired the IEEE T-MI Steering Committee and the ISBI Steering Committee. He was co-chair of the 1997 SPIE conference on Image Reconstruction and Restoration, technical program co-chair of the 2002 IEEE International Symposium on Biomedical Imaging (ISBI), and general chair of ISBI 2007. His research interests are in statistical aspects of imaging problems, and he has supervised doctoral research in PET, SPECT, X-ray CT, MRI, and optical imaging problems.

Sponsored by


Faculty Host

Dave Neuhoff