Enhanced Chemical Classification of Raman Images Using Wavelet Transformation
Tony Cai, Dongmao Zhang and Dor Ben-Amotz
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Abstract:
Multiresolution wavelet transformation (MWT) and block thresholding used to effectively
suppress both background and noise inteference 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 between 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.
- Paper:
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- Other related papers:
Cai, T. (1999).
Adaptive wavelet estimation: a block thresholding and oracle inequality approach.
The Annals of Statistics 27, 898-924.Cai, T. (2002).
On block thresholding in wavelet regression: Adaptivity, block size, and threshold level.
Statistica Sinica 12, 1241-1273.