Image edge detection enhancement using coefficients of Sakaguchi type functions mapped onto petal shaped domain

This research introduces a new approach to elevate the precision of image edge detection through a new algorithm rooted in the coefficients derived from the subclass $$ (CSKP model). Our method employs convolution operations on input image pixels, utilizing the CSKP mask window in eight distinct directions, fostering a comprehensive and multi-directional analysis of edge features. To gauge the efficacy of our algorithm, image quality is assessed through perceptually significant metrics, including contrast, correlation, energy, homogeneity, and entropy. The study aims to contribute a valuable tool for diverse applications such as computer vision and medical imaging by presenting a robust and innovative solution to enhance image edge detection. The results demonstrate notable improvements, affirming the potential of the proposed algorithm to advance the current state-of-the-art in image processing.