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Soft Computing
Erschienen in: Soft Computing 16/2019

28.06.2018 | Methodologies and Application

Solving reverse emergence with quantum PSO application to image processing

verfasst von: S. Djemame, M. Batouche, H. Oulhadj, P. Siarry

Erschienen in: Soft Computing | Ausgabe 16/2019

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Abstract

A quantum-inspired PSO (QPSO) algorithm for solving reverse emergence is proposed that is a hybridization of the particle swarm optimization (PSO) algorithm and quantum computing principles. For potential applications, we review specific image processing problems including image denoising and edge detection. Taking cellular automata as a modeling tool, an evolutionary process carried out by the QPSO algorithm attempts to extract the rules resulting in satisfactory image denoising and edge detection. Experimental results demonstrate the feasibility, the convergence and robustness of the QPSO algorithm for solving reverse emergence in the specific application of image processing.
Vorheriger Artikel Multi-attribute decision-making based on soft set theory: a systematic review
Nächster Artikel An efficient ID-based cryptographic transformation model for extended chaotic-map-based cryptosystem

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Metadaten
Titel
Solving reverse emergence with quantum PSO application to image processing
verfasst von
S. Djemame
M. Batouche
H. Oulhadj
P. Siarry
Publikationsdatum
28.06.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 16/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3331-6

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