Applying segmentation and classification to improve performance of smoothing

Abstract

Combining segmentation, classification, and time-fractional diffusion filtering, an excellent smoothing method of peak-preserving is proposed. First, the signal is divided into equal-length segments. Second, these segments are classified according to the similarity. Third, similar segments in the same class are stacked into a two-dimensional array, and then they are filtered by the two-dimensional discrete cosine transform. As a result, a preliminary smoothed signal is obtained. Finally, the preliminary smoothed signal is filtered in the time domain using the time-fractional diffusion filtering for peak-preserving. Thus, the final smoothed signal can be obtained. As a validation of the proposed method, comparisons are performed with different commonly-used methods such as the time-fractional diffusion method (TFDM), regularized method (RegM), Savitzky-Golay method (SGM) and wavelet method (WM). The results show that the proposed method has a better signal-to-noise ratio (SNR) and root-mean-square error (RMSE) than other classical filtering methods.