The search for efficient image de-noising methods still is a valid challenge, at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions, but fail in general and create artifacts or remove image structures. The main focus of this dissertation is first to define an algorithm and based on that a non-linear adaptive filter is described which not only preserve the actual image structures in the presence of different types of noises (Salt & peppers &Zero-Mean Gaussian White Noise) as well as it provide better PSNR, MSE & MAE than other classic algorithms (Mean, Kaun, Fourth order differential) which will be clearly shown in result section.
