Multiple Wavelet Threshold Estimation by Generalized Cross Validation for Data with Correlated Noise

De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure does not require an estimation for the noise energy. Originally, this method assumes uncorrelated noise. In this paper we describe how we can extend it to images with correlated noise.