A criterion for automatic image deconvolution with L-0-norm regularization

Ahmad, Mohamad; Hugelier, Siewert; Vitale, Raffaele; Eilers, Paul; Ruckebusch, Cyril

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1002/cem.3227

Permalink :

http://hdl.handle.net/20.500.12210/109595

Title :

A criterion for automatic image deconvolution with L-0-norm regularization

Author(s) :

Ahmad, Mohamad [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Hugelier, Siewert [Auteur]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Eilers, Paul [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Ruckebusch, Cyril [Auteur]

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Journal title :

Journal of Chemometrics

Abbreviated title :

J. Chemometr.

Volume number :

Publication date :

2020-02-29

ISSN :

0886-9383

English keyword(s) :

sparsity
regularization parameter
optimization
image
deconvolution

HAL domain(s) :

Chimie/Chimie théorique et/ou physique

English abstract : [en]

Automatic penalty adjustment in sparse deconvolution with penalized least squares is required for improved reliability and broader applicability. In sparse deconvolution with an L0-norm penalty, the latent signal is by ...
Show more >Automatic penalty adjustment in sparse deconvolution with penalized least squares is required for improved reliability and broader applicability. In sparse deconvolution with an L0-norm penalty, the latent signal is by nature discontinuous, and the magnitudes of the residuals and sparsity regularization terms are of different order of magnitude. This makes approaches such as generalized cross validation or L-curve unsuitable in practice. The criterion proposed in this paper is based on the representation of the sum of the normalized residuals and regularization terms (SNT) as a function of the penalty parameter. We observed that the minimum of the SNT corresponds to the optimal value of the penalty parameter. This approach was tested in the context of super-resolution fluorescence microscopy imaging. Both simulated and real live-cell images characterized by different complexities and emitter densities were analyzed to assess the performance of the developed optimization strategy and to demonstrate its usefulness over manual tuning.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS

Collections :

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Submission date :

2024-02-28T23:19:39Z
2024-03-12T14:31:21Z

Version	Link	Modification date
2	20.500.12210/109595*	2024-03-12T14:29:02Z
1	20.500.12210/109595.1	2024-02-28T23:19:39Z

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A criterion for automatic image deconvolution ...

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