M
Michael Elad
Researcher at Technion – Israel Institute of Technology
Publications - 356
Citations - 65399
Michael Elad is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Sparse approximation & Image processing. The author has an hindex of 87, co-authored 349 publications receiving 59428 citations. Previous affiliations of Michael Elad include Stanford University & Google.
Papers
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Journal ArticleDOI
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Journal ArticleDOI
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Michael Elad,Michal Aharon +1 more
TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.
Journal ArticleDOI
Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization
David L. Donoho,Michael Elad +1 more
TL;DR: This article obtains parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems, and sketches three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.
Book ChapterDOI
On single image scale-up using sparse-representations
TL;DR: This paper deals with the single image scale-up problem using sparse-representation modeling, and assumes a local Sparse-Land model on image patches, serving as regularization, to recover an original image from its blurred and down-scaled noisy version.
Journal ArticleDOI
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
TL;DR: The aim of this paper is to introduce a few key notions and applications connected to sparsity, targeting newcomers interested in either the mathematical aspects of this area or its applications.