Titre :

Using saliency detection to improve multi-focus image fusion

Auteur(s) :

Babahenini, Sarra [Auteur]
Charif, Fella [Auteur]
Cherif, Foudil [Auteur]
Département d'Informatique [FSESNV-Biskra]
Tahleb Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Ruichek, Yassine [Auteur]
Connaissance et Intelligence Artificielle Distribuées [Dijon] [CIAD]

Titre de la revue :

International Journal of Signal and Imaging Systems Engineering

Pagination :

81 - 92

Éditeur :

Inderscience

Date de publication :

2021-10-04

ISSN :

1748-0698

Mot(s)-clé(s) en anglais :

human vision system
visual saliency detection
saliency map
multi-focus
image fusion
weight map
contourlet transform
matrix decomposition.

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

In this paper, we introduce a novel and efficient algorithm based on saliency detection methods, our main contribution is a new manner to calculate the weight map by normalising the saliency values obtained from the input ...
Lire la suite >In this paper, we introduce a novel and efficient algorithm based on saliency detection methods, our main contribution is a new manner to calculate the weight map by normalising the saliency values obtained from the input images, which makes it possible to differentiate the focused and defocused regions. We have experiment three techniques of computing the weight map using contourlet transform and low-rank and structured sparse matrix decomposition (LSMD) model. The performance of the proposed model is compared with that of the state-of-the-art multi-focus fusion methods by using fusion metrics. Our evaluation of a series of dataset image demonstrate that the proposed method provides an improvement both visual quality and objective assessment compared to existing methods.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL