Sparse Representations in Image Processing: From Theory to Practice
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Brief Introduction
Learn about the deployment of the sparse representation model to signal and image processing.
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 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.
Knowledge
- 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.
Machine Learning & Deep Learning in Python & R
- 4.4
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Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R You've found the right Machine Learning course!...
Sparse Representations in Signal and Image Processing: Fundamentals
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Learn about the field of sparse representations by understanding its fundamental theoretical and algorithmic foundations. We will touch on theory and numerical algorithms....
Sparse Representations in Image Processing: From Theory to Practice
- 0.0
-
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Learn about the deployment of the sparse representation model to signal and image processing. This course is a follow-up to the first introductory course of sparse representations....
Probabilistic Graphical Models 1: Representation
- 4.6
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Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other....
