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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2015

01.02.2015 | Original Article

Image enhancement based on fractional directional derivative

verfasst von: Chaobang Gao, Jiliu Zhou, Chang Liu, Qiang Pu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2015

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Abstract

This paper introduces the directional derivative to fractional derivative and proposes a new mathematical method, fractional directional derivative (FDD), and gives the corresponding numerical calculation. Compared with the traditional fractional derivative, the coefficients of FDD along the eight directions in the image plane are not the same, which can reflect different fractional change rates along different directions and is benefit to enlarge the differences among the image textures. Experiments show that the capability of nonlinearly enhancing texture details by FDD is more obvious than those by the traditional fractional derivative and integer-order differentiation operators Laplacian, Butterworth high-pass filter.
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Metadaten
Titel
Image enhancement based on fractional directional derivative
verfasst von
Chaobang Gao
Jiliu Zhou
Chang Liu
Qiang Pu
Publikationsdatum
01.02.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0247-z

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