Development of mathematical tools for image restoration with distorted data

Abstract

This paper addresses image restoration in the presence of Gaussian noise, salt and pepper noise, occlusion and blur. A review of mathematical methods-from Fourier filters and statistical hypothesis testing to regression trees-is presented. The research aims to develop a unified framework for image quality assessment integrating statistical rigor with computational techniques.