A Novel Fractional Edge Detector Based on Generalized Fractional Operator

Authors

  • Diaa Diaa Eldin Elgezouli King Saud university
  • Mohamed A.Abdoon king saud university
  • Samir Brahim Belhaouari Hamad Bin Khalifa University
  • Dalal Khalid Almutairi Majmaah University

DOI:

https://doi.org/10.29020/nybg.ejpam.v17i2.5141

Keywords:

Fractional-order, Edge detection, Finite difference

Abstract

This work pioneers a novel approach in image edge detection through the utilization of the generalized fractional operator. By harnessing the global attributes inherent in fractional derivatives, it aims to enhance the extraction of intricate edge details.This is accomplished by creating the mask by using fractional derivative and adapt the mask by another parameter, yielding compelling and informative edge representations, as validated by experimental results. This advancement not only augments computer vision and image analysis techniques but also holds promise for refining image processing methodologies. Future endeavors may explore its adaptability across diverse imaging domains like medical and satellite imagery, while integration into deep learning frameworks could elevate its potential for advanced feature extraction and deeper image understanding. Additionally, optimizing its computational efficiency would broaden its scope for real-time deployment in fields such as robotics and autonomous systems. 

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Published

2024-04-30

Issue

Section

Nonlinear Analysis

How to Cite

A Novel Fractional Edge Detector Based on Generalized Fractional Operator. (2024). European Journal of Pure and Applied Mathematics, 17(2), 1009-1028. https://doi.org/10.29020/nybg.ejpam.v17i2.5141

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