Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data. The idea is to transform the data into the wavelet basis, where the "large" coefficients are mainly the signal, and the "smaller" ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. We evaluate several 2-D denoising procedures using test images corrupted with additive Gaussian noise. We consider global, level-dependent, and subband-dependent implementations of these techniques. Our results, using the mean squared error as a measure of the quality of denoising, show that the SureShrink and the BayesShrink methods consistently outperform the other wavelet-based techniques. In contrast, we found that a combination of simple spatial filters lead to images that were grainier with smoother edges, though the error was smaller than in the wavelet-based methods.