Wavelet-based denoising and baseline correction for enhancing chemical detection

Various chemical agents have been known to provide unique Raman spectrum signatures. Practical methods for chemical detection have to deal with cluttered data where the desired agent's signature is mixed with those of other chemicals in the immediate environment. It has been found that unmixing is affected by strong background signatures, such as those from the substrate, and noise. This work investigates use of wavelet transform based techniques for denoising and baseline correction for the purpose of enhancing the probability of detection of a desired agent.