Sparse Representations in Image Processing: From Theory to Practice

MOOC
Sparse Representations in Image Processing: From Theory to Practice
Language
English
Duration
3 months
Certificate
Certification paid
Course by EdX
Sparse Representations in Image Processing: From Theory to Practice
What will you learn?
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.
About the course

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.

Program
Sparse Representations in Image Processing: From Theory to Practice
Learn about the deployment of the sparse representation model to signal and image processing.
Lecturers
Michael Elad
Michael Elad
Professor of Computer Science, Technion Israel Institute of Technology
Alona Golts
Alona Golts
Lecturer IsraelX
Platform
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