Image Edge Detection Enhancement Using Coefficient Estimates for Classes of Quasi-Subordination: Fekete-Szego Problems

This paper investigates coefficient estimates for quasi-subordination classes and their application to enhance edge detection in image processing. We develop a Python-based algorithm utilizing Sakaguchi-inspired methods and Fekete-Szeg ̈o coefficient principles to improve edge clarity and boundary precision. The algorithm processes input images to produce outputs with highlighted edges, achieving increased accuracy and noise resilience. Fixed-point theory ensures the existence and uniqueness of solutions, while stability analysis confirms the robustness of the framework. Numerical simulations compare the proposed method with classical edge detectors (Sobel, Canny, Laplacian), demonstrating superior performance in capturing subtle features in complex scenes. These results highlight the potential of geometric function theory in advancing computational imaging techniques.