Image denoising is a common procedure in digital image processing aiming at the removal of noise, which may corrupt an image during its acquisition or transmission, while retaining its quality. This procedure is traditionally performed in the spatial or frequency domain by filtering. Recently, a lot of methods have been reported that perform denoising on the Discrete Wavelet Transform (DWT) domain. The transform coefficients within the subbands of a DWT can be locally modeled as i.i.d (independent identically distributed) random variables with Generalized Gaussian distribution. Some of the denoising algorithms perform thresholding of the wavelet coefficients, which have been affected by additive white Gaussian noise, by retaining only large coefficients and setting the rest to zero. However, their performance is not sufficiently effective as they are not spatially adaptive. Some other methods evaluate the denoised coefficients by an MMSE (Minimum Mean Square Error) estimator, in terms of the noised coefficients and the variances of signal and noise. The signal variance is locally estimated by a ML (Maximum Likelihood) or a MAP (Maximum A Posteriori) estimator in small regions for every subband where variance is assumed practically constant. These methods present effective results but their spatial adaptivity is not well suited near object edges where the variance field is not smoothly varied. The optimality of the selected regions where the estimators apply has been examined in some research works. This paper evaluates some of the wavelet domain algorithms as far as their subjective or objective quality performance is concerned and examines some improvements.