Курс Sparse Representations in Image Processing: From Theory to Practice

Обучение бесплатное
Сертификация платная
3 месяца
О курсе

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.

Sparse Representations in Image Processing: From Theory to Practice
Learn about the deployment of the sparse representation model to signal and image processing.
Что Вы изучите?
  • The importance of models in data processing, and the universality of sparse representation modeling.
  • Dictionary learning algorithms and their role in applications.
  • How to deploy sparse representations to signal and image processing tasks.
Michael Elad
Michael Elad
Professor of Computer Science, Technion Israel Institute of Technology
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.
Alona Golts
Alona Golts
Lecturer IsraelX
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.
Эта платформа предоставляет все курсы бесплатно. Авторами выступают топовые университеты и корпорации, которые стараются удерживать стандарты качества. За несоблюдение дедлайнов, невыполнение домашнего задания студенты теряют баллы. Как и в других платформах, лекционные видео чередуются с практическими заданиями. Обучение проводится на английском, китайском, испанском, французском и хинди.
Sparse Representations in Image Processing: From Theory to Practice