Réduction du nombre de niveaux de lissage dans une structure de filtre LUM FTC

Réduction du nombre de niveaux de lissage dans une structure de filtre LUM FTC

Reduction of smoothing levels in LUM FTC filter structure

Rastislav Lukác Viktor Fischer  Nathalie Bochard 

Slovak Image Processing Center, Jarkova 343, 049 25 Dobsina, Slovakia.

Laboratoire Traitement du Signal et Instrumentation, Unité Mixte de Recherche CNRS 5516, Université Jean Monnet, Saint-Etienne, France

Corresponding Author Email: 
1 July 2000
29 February 2004
| Citation



In this paper, we analyze the possibility of the reduction of smoothing levels in 3-D adaptive lower-upper-middle (LUM) smoother based on the fixed threshold control (FTC). Besides the excellent noise attenuation capability with the simultaneous signal-detail preservation, recently introduced LUM FTC filter with a window size N is characterized by a relatively complex structure, where an estimate is formed according to (N +1)/2 decision rules. This fact can constrain its possible hardware filter implementation in real motion video applications. In order to simplify the filter complexity, however, to retain the excellent filter performance simultaneously, we propose two approaches such as linear reduction of smoothing levels and optimal reduction based on genetic algorithm.


Dans cet article, nous analysons la possibilité de réduire le nombre de niveaux de lissage d’un filtre LUM (lower-upper-middle) adaptatif 3-D basé sur un contrôle par seuils fixes (FTC = fixed threshold control). Outre son excellente capacité d’atténuation du bruit tout en assurant la conservation des détails, le filtre LUM FTC avec une fenêtre de taille N, est caractérisé par une structure relativement complexe, où l’estimation de la valeur de sortie est faite en fonction de (N + 1)/2 règles de décision. Ceci peut entraver l’implémentation matérielle de tels filtres dans des applications vidéo temps réel. Afin de simplifier la complexité du filtre tout en gardant ses excellentes performances, nous proposons deux approches qui sont la réduction linéaire du nombre de niveaux de lissage et la réduction optimale basée sur un algorithme génétique.


Image sequences, order statistics, LUM smoother, adaptive filter, impulse noise

Mots clés

Séquences d’images, statistiques d’ordre, filtre LUM, filtre adaptatif, bruit impulsionnel

1. Introduction
2. Filtre LUM FTC
3. Réduction De La Complexité Du Filtre
4. Résultats Expérimentaux
5. Conclusion

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