Noise reduction algorithms were developed in order to improve Raman spectroscopy signals while preserving signal information. Two kinds of noise affect the measurements, impulsive noise caused by high-energy radiation and cosmic rays, and random noise produced in Raman backscattering estimation. Two sequential stages were developed to process the Raman images, the first stage attempts to remove the impulsive noise and the second one reduces the random noise. The explored algorithms have been tested using real data as well as synthetic data, and compared using the Means Squared Error (MSE) and the Infinity Norm (Linfinity). In terms of random noise reduction, results show Wavelet denoising performs better, in the sense of MSE and L infinity, compared to Kalman filters and the widely used Savitzky-Golay filter.
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