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Neural Adaptive Fractional Order Differential ...
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Document type :
Communication dans un congrès avec actes
DOI :
10.1109/SIVA.2018.8661060
Title :
Neural Adaptive Fractional Order Differential based Algorithm for Medical Image Enhancement
Author(s) :
Krouma, Houda [Auteur]
Ferdi, Youcef [Auteur]
Taleb-Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Conference title :
2018 International Conference on Signal, Image, Vision and their Applications (SIVA )
City :
Guelma
Country :
Algérie
Start date of the conference :
2018-11-26
Publisher :
IEEE
English keyword(s) :
"image enhancement"
"fractional differential calculus"
"artificial neural network"
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Informatique [cs]/Intelligence artificielle [cs.AI]
Sciences de l'ingénieur [physics]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
English abstract : [en]
In this paper, we propose an adaptive fractional differential calculus based technique for image enhancement. The adaptive fractional order used in the fractional differential mask is computed through a neural network based ...
Show more >
In this paper, we propose an adaptive fractional differential calculus based technique for image enhancement. The adaptive fractional order used in the fractional differential mask is computed through a neural network based scheme. The training of the neural network is achieved by using adaptive fractional orders calculated by means of AFDA (Adaptive Fractional Differential Approach) algorithm for different medical images. After training, the neural network calculates the appropriate adaptive fractional order that will be substituted in the mask to enhance the image. We perform some experiments on medical images then compare the enhancing performance with that of the AFDA algorithm, demonstrating that the proposed method leads to a better quality of enhanced images, giving rise to clearer edges and richer texture with less computational complexityShow less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520
Source :
Harvested from HAL
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