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Fractional differential mask: a fractional differential-based approach for multiscale texture enhancement

Authors:

Xiao YuanAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 19, Issue 2

Pages 491 - 511

https://doi.org/10.1109/TIP.2009.2035980

Published: 01 February 2010 Publication History

Abstract

In this paper, we intend to implement a class of fractional differential masks with high-precision. Thanks to two commonly used definitions of fractional differential for what are known as Grümwald-Letnikov and Riemann-Liouville, we propose six fractional differential masks and present the structures and parameters of each mask respectively on the direction of negative x-coordinate, positive x-coordinate, negative y-coordinate, positive y-coordinate, left downward diagonal, left upward diagonal, right downward diagonal, and right upward diagonal. Moreover, by theoretical and experimental analyzing, we demonstrate the second is the best performance fractional differential mask of the proposed six ones. Finally, we discuss further the capability of multiscale fractional differential masks for texture enhancement. Experiments show that, for rich-grained digital image, the capability of nonlinearly enhancing complex texture details in smooth area by fractional differential-based approach appears obvious better than by traditional intergral-based algorithms.

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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 19, Issue 2

February 2010

274 pages

ISSN:1057-7149

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Copyright © 2010.

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IEEE Press

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Published: 01 February 2010

Revised: 23 September 2009

Received: 22 October 2008

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Kong XYu FYao WCai SZhang JLin H(2024)Memristor-induced hyperchaos, multiscroll and extreme multistability in fractional-order HNNNeural Networks10.1016/j.neunet.2023.12.008171:C(85-103)Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.neunet.2023.12.008
Ramesh Babu NSam Joshua ABalasubramaniam PTiwari A(2024)Fuzzy-driven image enhancement via ABR-fractal-fractional differentiationInformation Sciences: an International Journal10.1016/j.ins.2024.120741675:COnline publication date: 1-Jul-2024
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