Abstract

Image denoising is a fundamental process in image processing, pattern recognition, and computer vision fields. The main goal of image denoising is to enhance or restore a noisy image and help the other system (or human) to understand it better. In this thesis, we discuss some efficient approaches for image denoising using wavelet transforms. Since Donoho proposed a simple thresholding method, many different approaches have been suggested for a decade. They have shown that denoising using wavelet transforms produces superb results. This is because wavelet transform has the compaction property of having only a small number of large coefficients and a large number of small coefficients. In the first part of the thesis, some important wavelet transforms for image denoising and a literature review on the existing methods are described. In the latter part, we propose two different approaches for image denoising. The first approach is to take advantage of the higher order statistical coupling between neighbouring wavelet coefficients and their corresponding coefficients in the parent level with effective translation-invariant wavelet transforms. The other is based on multivariate statistical modeling and the clean coefficients are estimated in a general rule using Bayesian approach. Various estimation expressions can be obtained by a priori probability distribution, called multivariate generalized Gaussian distribution (MGGD). The method can take into account various related information. The experimental results show that both of our methods give comparatively higher PSNR and less visual artifact than other methods.