Nonlinear filtering in the wavelet transform domain

A new approach for image denoising in the wavelet transform domain is proposed. In this approach we attempt to replace each wavelet coefficient by its expected value. For that we will use local neighboring coefficients to provide a measure of similarity, noise and edge classification. The approach uses the statistical characteristics of neighboring coefficients as well as the noise characteristics. A clustering technique is used to determine the degree of belonging of neighboring coefficients and coefficient under consideration. Experimental results show that this technique yields comparable results in removing Gaussian type noise. The results show that the approach yields far better results than other existing technique in removing both Gaussian and outlier type noise without disturbing important image features