Some Fixed Point Theorems and Applications to Noise Reduction in Signal Processing

Abstract

In this paper, we introduce the notion of a quasi-convex p-metric space and study some fixedpoint theorems for self-mappings satisfying certain contractive conditions in quasi-convex p metric space. Additionally, we establish fixed point theorems for generalized contractions in a quasi-convex p-metric space. This result generalizes previous related work in the literature. An application supports the theoretical analysis by demonstrating the use of quasi convex p-metric spaces in signal processing for noise reduction and signal smoothing. This method introduces an adaptive technique that needs to be modeled and minimized to achieve noise reduction.