Skip to Main content Skip to Navigation
Conference papers

Optimization of a Geman-McClure like criterion for sparse signal deconvolution

Abstract : This paper deals with the problem of recovering a sparse unknown signal from a set of observations. The latter are obtained by convolution of the original signal and corruption with additive noise. We tackle the problem by minimizing a least-squares fit criterion penalized by a Geman-McClure like potential. The resulting criterion is a rational function, which makes it possible to formulate its minimization as a generalized problem of moments for which a hierarchy of semidefinite programming relaxations can be proposed. These convex relaxations yield a monotone sequence of values which converges to the global optimum. To overcome the computational limitations due to the large number of involved variables, a stochastic block-coordinate descent method is proposed. The algorithm has been implemented and shows promising results
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01263352
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Monday, February 15, 2016 - 5:28:38 PM
Last modification on : Friday, May 29, 2020 - 4:34:09 PM
Document(s) archivé(s) le : Saturday, November 12, 2016 - 9:30:17 PM

File

camsap15.pdf
Files produced by the author(s)

Identifiers

Citation

Marc Castella, Jean-Christophe Pesquet. Optimization of a Geman-McClure like criterion for sparse signal deconvolution. CAMSAP 2015 : 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2015, Cancun, Mexico. pp.317-320, ⟨10.1109/CAMSAP.2015.7383798⟩. ⟨hal-01263352v2⟩

Share

Metrics

Record views

292

Files downloads

501