Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise

Denoising algorithms based on wavelet thresholding replace small wavelet co efficients by zero and keep or shrink the coefficients with absolute value above the threshold. The optimal threshold minimizes the error of the resul t as compared to the unknown, exact data. To estimate this optimal threshol d, we use generalized cross validation. This procedure does not require an estimation for the noise energy. Originally, this method assumes uncorrelat ed noise. In this paper, we describe how we can extend it to images with co rrelated noise.