An Improved Grunwald-Letnikov Fractional Differential Mask for Image Texture Enhancement
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International Journal of Advanced Computer Science and Applications(IJACSA) (2012)

Texture plays an important role in identification of objects or regions of interest in an image. In order to enhance this textural information and overcome the limitations of the classical derivative operators a two-dimensional fractional differential operator is discussed, which is an improved version of the Grunwald-Letnikov (G-L) based fractional differential operator. A two dimensional-isotropic gradient operator mask based on G-L fractional differential is constructed. This nonlinear filter mask is implemented on various texture enriched digital images and enhancement of features of image is controlled by varying the intensity factor. In order to analyze the enhancement quantitatively, information entropy and average gradient are the parameters used. The results show that with improved version of Grunwald-Letnikov, fractional differential operator information entropy of image is improved by 0.5.
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