Sparse Representations in Image Processing
From Theory to Practice
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Sparse Representations in Signal and Image Processing,
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.
What you'll 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.
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| Rating | 5.0★ based on 6 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 5 - 6 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $149 |
| From | IsraelX, Technion via edX |
| Instructors | Michael Elad, Yaniv Romano, Michael Elad, Alona Golts |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Mathematics |
| Tags | Data Analysis & Statistics Math Engineering Technion |
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What people are saying
linear algebra
An excellent course for those with knowledge of: Linear Algebra, DSP, and Probability.
You need a good working knowledge of linear algebra to succeed.
Read more
fall right into place
All pieces are given and fall right into place fro theory to practice.
practical projects as well
I really enjoyed the practical projects as well.
really enjoyed the practical
coursera before taking
I recommend Guillermo Shapiro's MOOC "Image and Video Processing: From Mars to Hollywood with a Stop at the Hospital" on Coursera before taking this one.
completely pays off
It was a very challenging course but it completely pays off.
everyone can build
While this is clearly and advance course, I think all pieces were given so that everyone can build a general picture of sparse-land.
place fro theory
researchers world wide
I want to thank Prof. Elad for sharing all his knowledge with students and researchers world wide.
elads book closely
It follows Prof. Elads book closely.
from mars
redundant representations
It follows Michael Elad's textbook "Sparse and Redundant Representations" closely.
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Image Specialist 1 $43k
Image Clerk $43k
Image Contributor $53k
Advanced Image Processing Spec $57k
Image Specialist 3 $57k
Image Processing Software Engineer $59k
Image Processing Algorithm Engineer $66k
Image Processing/Computer Vision Specialist TS/SCI $69k
Image Management $71k
SME - Image Signal Processing Pipeline $71k
Advanced Image Processing Specialist $77k
Image-Signal Processing Engineer $80k
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| Rating | 5.0★ based on 6 ratings |
|---|---|
| Length | 5 weeks |
| Effort | 5 - 6 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $149 |
| From | IsraelX, Technion via edX |
| Instructors | Michael Elad, Yaniv Romano, Michael Elad, Alona Golts |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Mathematics |
| Tags | Data Analysis & Statistics Math Engineering Technion |
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