A bi‐directional fractional‐order derivative mask for image processing applications
Meriem Hacini, Fella Hachouf, Abdelfatah Charef
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.