| Key info |
| Offered by | Technion Israel Institute of Technology |
| Description | In this course, you learn how to use sparse representations in series of image processing tasks. You learn about 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" be presented, along with ways to use the trained dictionaries in the above mentioned applications.
WHAT YOU WILL 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.
- Sparseland theoretic and algorithmic background.
- Introduction to image priors and their evolution in image processing.
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| Accredited by | edX - online learning platform |
| URL |
https://www.edx.org/course/sparse-representations-in-image-processing-from-th
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