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Sparse Representations in Image Processing - From Theory to Practice, Certificate

  • Anytime
    Application Deadline
  • 35 days
EdX is an online learning platform trusted by over 12 million users offering the Sparse Representations in Image Processing - From Theory to Practice Certificate in collaboration with IsraelX. Learn about the deployment of the sparse representation model to signal and image processing.


The Sparse Representations in Image Processing - From Theory to Practice Certificate is part of the Sparse Representations in Signal and Image Processing Professional Certificate from EdX in partnership with IsraelX.

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.

Detailed Programme Facts

  • Programme intensity Part-time
    • Average part-time duration 35 days
    • Part-time variant
    • Duration description
      • Length: 5 weeks 
      • Effort: 5-6 hours per week
  • Languages
    • English
  • Delivery mode

Programme Structure

This program is composed from two separate parts:

  • Part 1: Sparse Representations in Signal and Image Processing: Fundamentals.
  • Part 2: Sparse Representations in Image Processing: From Theory to Practice.

While we recommend taking both courses, each of them can be taken independently of the other. The duration of each course is five weeks, and each part includes: 

  •  knowledge-check questions and discussions, 
  • series of quizzes, and 
  •  Matlab programming projects. 


  • Michael Elad - Professor of Computer Science, Technion, Israel Institute of Technology

  • Yaniv Romano - PhD student in the Electrical Engineering Department, Technion, Israel Institute of Technology

English Language Requirements

This programme may require students to demonstrate proficiency in English.

Academic Requirements


  • Advanced knowledge on linear algebra and optimization; basic familiarity with signal and image processing.

Tuition Fee

  • International

    99 USD/full
    Tuition Fee
    Based on the original amount of 99 USD for the full programme and a duration of 35 days.
  • National

    99 USD/full
    Tuition Fee
    Based on the original amount of 99 USD for the full programme and a duration of 35 days.
We've labeled the tuition fee that applies to you because we think you are from and prefer over other currencies.


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