IMPROVEMENT OF SIMS IMAGE CLASSIFICATION BY MEANS OF WAVELET DE-NOISING

Classification is a powerful tool for the extraction of chemical infor mation from analytical images. However, classification may sometimes n ot be applicable, when the clusters corresponding to single phases ove rlap due to high noise levels, such that they are not significantly di stinguishable. We investigate the effect of the wavelet shrinkage de-n oising algorithm on the subsequent classification of analytical images . By application of wavelet de-noising the distinction of phases in cl assification can be significantly improved.