Learning Sparsifying Transforms for Signal, Image, and Video Processing
This event is hosted/sponsored by IEEE SPS Chapter and co-sponsored by IEEE SSCS & ITS Chapters.
Speakers (Distinguished Lecturer):
Prof. Yoram Bresler
Departments of Electrical and Computer Engineering and Bioengineering
University of Illinois at Urbana-Champaign
AMD Commons C-6/7/8, 991 Stewart Dr., Sunnyvale, CA (map or Google Maps)
6:30pm: Networking/Light Dinner
Free. Donation accepted for food.
The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal and image processing, including compression, denoising, and notably in compressed sensing, which enables accurate reconstruction from undersampled data. These various applications used sparsifying transforms such as DCT, wavelets, curvelets, and finite differences, all of which had a fixed, analytical data-independent form.
Recently, sparse representations that are directly adapted to the data have become popular, especially in applications such as image and video denoising and inpainting. While synthesis dictionary learning has enjoyed great popularity and analysis dictionary learning too has been explored, these methods involve a repeated step of sparse coding, which is NP hard, and heuristics for its approximation are computationally expensive. In this talk we describe our work on an alternative approach: sparsifying transform learning, in which a sparsifying transform is learned from data. The method provides efficient computational algorithms with exact closed-form solutions for the alternating optimization steps, and with theoretical convergence guarantees. The method scales better than dictionary learning with problem size and dimension, and in practice provides orders of magnitude speed improvements and better image quality in image processing applications. Variations on the method include the learning of a union of transforms, and online versions.
We describe applications to image representation, image and video denoising, and inverse problems in imaging, demonstrating improvements in performance and computation over state of the art methods.
Yoram Bresler received the B.Sc. (cum laude) and M.Sc. degrees from the Technion, Israel Institute of Technology, in 1974 and 1981 respectively, and the Ph.D degree from Stanford University, in 1986, all in Electrical Engineering. In 1987 he joined the University of Illinois at Urbana-Champaign, where he is currently a Professor at the Departments of Electrical and Computer Engineering and Bioengineering, and at the Coordinated Science Laboratory. He is also President and Chief Technology Officer at InstaRecon, Inc., a startup he co-founded to commercialize breakthrough technology for tomographic reconstruction developed in his academic research. His current research interests include multi-dimensional and statistical signal processing and their applications to inverse problems in imaging, and in particular compressed sensing, computed tomography, and magnetic resonance imaging.
Dr. Bresler has served on the editorial board of a number of journals, including the IEEE Transactions on Signal Processing, the IEEE Journal on Selected Topics in Signal Processing, Machine Vision and Applications, and the SIAM Journal on Imaging Science, and on various committees of the IEEE. Dr. Bresler is a fellow of the IEEE and of the AIMBE. He received two Best Journal Paper Awards from the IEEE Signal Processing society, and a paper he coauthored with one of his students received the Young Author Award from the same society in 2002. He is the recipient of a 1991 NSF Presidential Young Investigator Award, the Technion (Israel Inst. of Technology) Fellowship in 1995, and the Xerox Senior Award for Faculty Research in 1998. He was named a University of Illinois Scholar in 1999, appointed as an Associate at the Center for Advanced Study of the University in 2001-2, and Faculty Fellow at the National Center for Supercomputing Applications (NCSA) in 2006. In 2016 he was appointed an IEEE Signal Processing Society Distinguished Lecturer.