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:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
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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