Riesz Fractional Derivative-Based Approach for Texture Enhancement

Textural enhancement is an indispensable area that needs to be addressed in the case of applications of image processing. Despite the existence of various enhancement approaches, it is quite difficult to preserve the informational details while enhancing the image features. This creates the necessity for the development of a fractional derivative approach for enhancing the image texture. This paper develops a unique mask by utilizing the Riesz fractional derivative (RFD) to enhance fine details in images. Simulations are conducted on the test images from standard datasets and fundus images to establish the efficacy of presented RFD method by metrics such as average gradient (AG) and information entropy (IE). The simulated results show the proficiency of the presented RFD approach as the improvement of 0.0649 and 6.3327 is achieved for IE and AG for the standard images against the existing methods.