A bi‐directional fractional‐order derivative mask for image processing applications. Issue 11 (14th August 2020)

• Record Type: Journal Article
• Authors: Hacini, Meriem; Hachouf, Fella; Charef, Abdelfatah
• Is Part Of: IET image processing. Volume 14:Issue 11(2020)
• Page Start: 2512
• Page End: 2524
• Publication Date: 2020-08-14
• Subjects: edge detection -- differentiation -- image denoising -- image texture -- gradient methods; de‐noising problems -- fractional useful operators -- fractional edge images -- image processing applications -- fractional computation -- two‐dimensional fractional differentiation -- one‐dimensional Charef fractional differentiation extension -- multidirectional mask -- adaptive fractional‐order computation -- gradient computation properties -- edge detection -- bi‐directional fractional‐order derivative mask; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0467
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Fractional computation has been recently designed as a major mathematical tool in image and signal processing fields. This study presents a novel operator established for two‐dimensional fractional differentiation. It is developed based on the one‐dimensional Charef fractional differentiation extension. A new multi‐directional mask is proposed and a new adaptive fractional‐order computation is introduced. The proposed method uses the gradient computation properties. It has been applied in edge detection and de‐noising problems using real and synthetic images. Obtained results have been compared to those given by integer and fractional useful operators. Results demonstrate that the fractional edge images obtained using the proposed operator has more complete and clear contour information and more abundant texture detail information. The performances have been improved by the proposed method.