Wavelet Based Color Image Denoising through a Bivariate Pearson Distribution

Abstract

In this paper we proposed an efficient algorithm for Colo r Image Denoising through a Bivariate Pearson Distribution using Wavelet Which is based on Bayesian denoising and if Bayesian denoising is used for recovering image from the noisy image the performance is strictly depend on the correctness of the distribution that is used to describe the data. In the denoising process we require a selection of p roper model for distribution. To describe the image data bivariate pearson distribution is used and Gaussian distribution is used to describe the noise particles in this paper. For gray scale image lots of extensive works has been don e in this field but fo r colour image denoising using bivariate pearson distribution based on bayesian denoising gives us tremendous result for analy sing coloured images which can be used in several advanced applications. The bivariate probability density function (pdf) takes in t o account the Gaussian dependency among wavelet coefficients. The experimental results show that the proposed technique outperforms sev eral exiting methods both visually and in terms of peak signal - to - noise ratio (PSNR).