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Donoho Statistics. Since he holds a permanent faculty position in the Computer Science Department at the Technion.

Sparse Representations and Their Applications in Signal and Image Processing

Michael works in the field of signal and image processing, specializing in particular on inverse problems and sparse representations. He has authored hundreds of technical publications in leading venues, many of which have led to exceptional impact. Michael received numerous teaching and research awards and grants. He was awarded an ERC advanced grant in He is the recipient of the and Henri Taub Prizes for academic excellence, the Hershel-Rich prize for innovation, and the Yanai prize for excellence in academic teaching.

Sparse Representations in Signal and Image Processing: Fundamentals - IsraelX on edX

Title: Sparse Modeling in Image Processing and Deep Learning Abstract: Sparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. We will, however, try to bring everybody on the same page in terms of the mathematical background required, mostly through reviews of the mathematical basics in the discussion sessions.

Michael Elad

Moreover, the lecture notes contain detailed material on the advanced mathematical concepts used in the course. If you are unsure about the prerequisites, please contact C.

Aubel or H. This course is an introduction to the field of applied harmonic analysis with emphasis on applications in signal processing such as transform coding, inverse problems, imaging, signal recovery, and inpainting. Frame theory: Frames in finite-dimensional spaces, frames for Hilbert spaces, sampling theorems as frame expansions Spectrum-blind sampling: Sampling of multi-band signals with known support set, density results by Beurling and Landau, unknown support sets, multi-coset sampling, the modulated wideband converter, reconstruction algorithms Sparse signals and compressed sensing: Uncertainty principles, recovery of sparse signals with unknown support set, recovery of sparsely corrupted signals, orthogonal matching pursuit, basis pursuit, the multiple measurement vector problem High-dimensional data and dimension reduction: Random projections, the Johnson-Lindenstrauss Lemma, the Restricted Isometry Property, concentration inequalities, covering numbers, Kashin widths.


  • From Theory to Applications in Signal and Image Processing!
  • 1. Introduction.
  • Recensie(s)?
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  • 227-0434-00L Harmonic Analysis: Theory and Applications in Advanced Signal Processing.

Elad, ''Sparse and redundant representations -- From theory to applications in signal and image processing'', Springer,