Sparse Representations in Image Processing: From Theory to Practice

Provided by:
9/10 stars
based on  6 reviews
Provided by:
Cost FREE , Add a Verified Certificate for $99
Start Date Upcoming
Sparse Representations in Image Processing: From Theory to Practice

Course Details

Cost

FREE,
Add a Verified Certificate for $99

Upcoming Schedule

  • Upcoming

Course Provider

edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

Provider Subject Specialization
Sciences & Technology
Business & Management
22043 reviews

Course Description

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 th...

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 course image
Reviews 9/10 stars
6 Reviews for Sparse Representations in Image Processing: From Theory to Practice

Ratings details

  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

Sort By
Stuart profile image
Stuart profile image

Stuart

10/10 starsCompleted
11 months, 4 weeks ago
This is the kind of course that makes applications appear everywhere, while retaining rigour and depth. The instructor is clear and the exercises reveal the power of what is presented.
Was this review helpful? Yes0
 Flag
Fabian profile image
Fabian profile image

Fabian

10/10 starsCompleted
12 months ago
I've learned a lot in this course. As already mentioned, this follows Prof. Elads book closely. If you're interested in this topic you should definitely take this course.
Was this review helpful? Yes0
 Flag
 profile image
 profile image

8/10 starsCompleted
  • 1 review
  • 0 completed
12 months ago
The instructor is very good . Content wise it would have been better if the course went beyond image processing only into signal processing in general especially speech. Also some content on convolutional sparse coding was expected . People not paying should be allowed to give all exams and get an audit certificate. Something I miss in Coursera and edx now but available at the beginning.
Was this review helpful? Yes0
 Flag
Carlos Ramirez profile image
Carlos Ramirez profile image
10/10 starsCompleted
  • 1 review
  • 1 completed
12 months ago
Amazing course! While this is clearly and advance course, I think all pieces were given so that everyone can build a general picture of sparse-land. IT was a rigorous and challenging course. Excellent over all.
Was this review helpful? Yes0
 Flag
student profile image
student profile image

student

10/10 starsCompleted
12 months ago
Excellent but advanced course. It follows Michael Elad's textbook "Sparse and Redundant Representations" closely. You need a good working knowledge of linear algebra to succeed. 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.
Was this review helpful? Yes0
 Flag
 profile image
 profile image

8/10 starsCompleted
  • 1 review
  • 1 completed
12 months ago
It will be great if there are more applied examples (MATLAB format) to help fixing course ideas. I think one project at each chapter will be great. One possibility is an automatic correction for the intermediate one.
Was this review helpful? Yes0
 Flag

Rating Details


  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.