DENOISING WITH GREEDY-LIKE PURSUIT ALGORITHMS - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2011

DENOISING WITH GREEDY-LIKE PURSUIT ALGORITHMS

Abstract

This paper provides theoretical guarantees for denoising per-formance of greedy-like methods. Those include Compres-sive Sampling Matching Pursuit (CoSaMP), Subspace Pur-suit (SP), and Iterative Hard Thresholding (IHT). Our resultsshow that the denoising obtained with these algorithms isa constant and a log-factor away from the oracle's perfor-mance, if the signal's representation is sufficiently sparse.Turning to practice, we show how to convert these algorithmsto work without knowing the target cardinality, and insteadconstrain the solution to an error-budget. Denoising tests onsynthetic data and image patches show the potential in thisstagewise technique as a replacement of the classical OMP.
Fichier principal
Vignette du fichier
EUSIPCO-Denoising-Guarantees.pdf (91.49 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00623895 , version 1 (15-09-2011)

Identifiers

  • HAL Id : inria-00623895 , version 1

Cite

Raja Giryes, Michael Elad. DENOISING WITH GREEDY-LIKE PURSUIT ALGORITHMS. The 19th European Signal Processing Conference (EUSIPCO‐2011), Aug 2011, Barcelona, Spain. ⟨inria-00623895⟩
83 View
64 Download

Share

Gmail Facebook X LinkedIn More