Denoising Raman spectra by Wiener estimation with a numerical calibration dataset

Most denoising methods that are currently used in the processing of Raman spectra require significant user interaction in order to optimize their performance across a range of signal-to-noise ratios. In this study, we proposed a method based on the principle of spectral integration followed by Wiener estimation using a numerical calibration dataset, which eliminates the need of experimental measurements for calibration as in the previous Wiener estimation based denoising method. The new method was tested on three types of samples, including a phantom sample, human fingernail and leukemia cells. Compared to two common denoising methods, i.e. moving-average filtering and Savitzky-Golay filtering, the performance of the proposed method is significantly less sensitive to the choices of parameters. Moreover, this method provides comparable or even better denoising performance in the cases with low signal-to-noise ratios.