קורס זה מוגש בשפה האנגלית באמצעות פלטפורמת edX ולכן דורש הרשמה נפרדת.
:What you will learn
- Fundamental theoretical contributions of sparse representation theory.
- The importance of models in data processing.
- Dictionary learning algorithms and their role in using this mode.
- How to deploy sparse representations to signal and image processing tasks.
This course is a follow-up to the first introductory course of sparse representations. Whereas the first course puts emphasis on the theory and algorithms in this field, this course shows how these apply to actual signal and image processing needs.
Models play a central role in practically every task in signal and image processing. Sparse representation theory puts forward an emerging, highly effective, and universal such model. Its core idea is the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements.
In this course, you will learn how to use sparse representations in series of image processing tasks. We will cover applications such as denoising, deblurring, inpainting, image separation, compression, super-resolution, and more. A key feature in migrating from the theoretical model to its practical deployment is the adaptation of the dictionary to the signal. This topic, known as "dictionary learning" will be presented, along with ways to use the trained dictionaries in the above mentioned applications.
עצמי: למידה בזמן שמתאים לכם.
מונחה: לוח הזמנים נבנה ע"י צוות הקורס.
Received the B.Sc. and M.Sc. degrees from the Department of Electrical Engineering, Technion—Israel Institute of Technology, Haifa, Israel, in 2010 and 2015, respectively. She is currently pursuing her Ph.D. in the Department of Computer Science in the Technion. Her research interests are deep learning, inverse problems, and sparse representations.
Yaniv Romano received his B.Sc. degree from the Department of Electrical Engineering, Technion – Israel Institute of Technology, in 2012, where he is currently pursuing his Ph.D.. He received the 2015 Zeff fellowship, the 2017 Andrew and Erna Finci Viterbi fellowship, and the 2017 Irwin and Joan Jacobs fellowship. In parallel to his studies, he has been working in the industry since 2011 as an Image Processing Algorithm Developer. The super-resolution technology he invented as a researcher in Google was launched in 2017, leading to significant bandwidth savings of billions of images.
Prof. Michael Elad
Prof. Michael Elad
Michael Elad received his B.Sc., M.Sc., and D.Sc. degrees from the Department of Electrical Engineering, Technion –Israel Institute of Technology, Israel, in 1986, 1988, and 1997, respectively. Since 2003 he is a Faculty at the Computer-Science Department, Technion. Prof. Elad received numerous teaching awards, and was a recipient of the 2008 and 2015 Henri Taub Prizes for academic excellence, and the 2010 Hershel-Rich prize for innovation. Prof. Elad is a fellow of IEEE. He is serving as the Editor-in-Chief of the SIAM Journal on Imaging Sciences since January 2016.