In this course, you will learn:
- Models are important in data processing, and sparse representation modeling is universal.
- The role of dictionary learning algorithms in applications.
- How to use sparse representations to solve signal and image processing problems.
- Overview of the field and this course.
- Sparseland theoretic and algorithmic background.
- Introduction to image priors and their evolution in image processing.
- In-depth view of the Sparseland model including a geometry perspective and processing of Sparseland' signals.
- Image deblurring and Iterative Shrinkage Thresholding Algorithm (ISTA).
- Sparesland from an estimation point of view, including a crash-course of estimation theory.
- The quest for a dictionary: choosing versus learning a dictionary, including basic dictionary learning algorithms: MOD and KSVD.
- Challenges in dictionary learning and advanced methods, including the double-sparsity, unitary and signature dictionaries.
- The image denoising problem and ways to solve it, including global and patch-based Sparseland methods.
- Crash course on SURE estimator for parameter tuning.
- The tasks of image separation and inpainting, including Morphological Component Analysis (MCA) and global versus patch-based treatment.
- The single image super-resolution problem and ways to solve it using Sparseland.
- Course summary and future research directions of the field.