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This is a course which emphasizes on the applications of theory and algorithms in this field. These representation models play a vital role in signal and imaging process.

Sparse Representation theory contributes to the modeling of data as a linear combination of building blocks. You will learn how to use sparse representations in a series of image processing tasks, image deblurring, inpainting, separation, compression and more in this course.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

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Course Credit: Israel-X

Course Curriculum

0.1 What This Field is All About Modeling Data 00:05:00
0.2 Sparseland Theoretical & Algorithmic Background 00:10:00
0.3 This Course Scope and Style 00:03:00
1.1 A Word About Notations 00:01:00
1.2 A Prior for Images How and Why 00:08:00
1.3 The Evolution of Priors in Image Processing 00:08:00
1.4 Linear vs. Non-Linear Approximation 00:07:00
1.5 The Sparseland Model 00:05:00
1.6 The Geometry behind Sparseland 00:04:00
1.7 Processing Sparseland’s Signals 00:07:00
1.8 Image-Deblurring via Sparseland Problem Formulation 00:05:00
1.9 Starting with Classical Optimization 00:04:00
1.10 Iterative Shrinkage Thresholding Algorithm (ISTA) 00:07:00
1.11 Shrinkage A Matlab Demo 00:02:00
1.12 Image Deblurring Results & Discussion 00:04:00
1.13 Image Deblurring A Closer Look at the Results 00:06:00
2.1 Background Choosing vs. Learning the Dictionary 00:07:00
2.2 Dictionary Learning (DL) Problem Formulation 00:07:00
2.3 The MOD Algorithm 00:04:00
2.4 The K-SVD Algorithm 00:06:00
2.5 Matlab Demo 00:08:00
2.6 Dictionary Learning Difficulties 00:06:00
2.7 The Double-Sparsity Algorithm 00:07:00
2.8 Learning Unitary Dictionaries 00:06:00
2.9 The Signature Dictionary 00:07:00
2.10 Dictionary Learning Summary 00:02:00
3.1 The Denoising Problem and Its Importance 00:07:00
3.2 First Steps in Image Denoising 00:05:00
3.3 Variations on the Global Thresholding Algorithm 00:02:00
3.4 SURE for Parameter Tuning The Theory 00:05:00
3.5 SURE for Parameter Tuning The Practice 00:03:00
3.6 Patch-Based Denoising – Basics 00:05:00
3.7 Patch-Based Denoising Theoretical Foundations 00:05:00
3.8 The K-SVD Image Denoising Algorithm 00:08:00
3.9 Patch-Based Denoising – Other Methods 00:08:00
3.10 Image Denoising – Summary 00:03:00
4.1 A Strange Experiment 00:07:00
4.2 A Crash-Course on Estimation Theory 00:05:00
4.3 Sparseland An Estimation Point of View 00:07:00
4.4 Sparseland Approximate Estimation 00:06:00
4.5 MMSE Back to Reality 00:04:00
5.1 Morphological Component Analysis The Core Idea 00:04:00
5.2 Cartoon-Texture Image Separation via a Global Treatment 00:04:00
5.3 From Separation to Inpainting A Global Approach 00:04:00
5.4 Patch-Based Image Separation 00:05:00
5.5 Patch-Based Image Inpainting 00:05:00
5.6 Patch-Based Impulse Noise Removal 00:03:00
5.7 Single-Image Super-Resolution First Steps 00:05:00
5.8 Single-Image Super-Resolution Detailed Algorithm 00:05:00
5.9 Single-Image Super-Resolution The Overall Algorithm 00:02:00
5.10 Single-Image Super-Resolution Results 00:03:00
6.1 Sparseland What is it all About from 00:04:00
6.2 Sparseland What is Still Missing 00:07:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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