Enhanced chemical classification of Raman images using multiresolution wavelet transformation

Multiresolution wavelet transformation (MWT) and block thresholding is used to effectively suppress both background and noise interference while minimally distorting Raman spectral features. The performance of MWT as a spectral pre-processing algorithm is demonstrated using both synthetic spectra and experimental hyper-spectral Raman images with large background and noise components. The results are quantified by comparing correlation coefficients of synthetic spectra with either the same or different backgrounds. The improved chemical imaging performance obtained using MWT is demonstrated by comparing principal component analysis (PCA) channel images and spectral angle mapping (SAM) classified images before and after MWT pre-processing.