A New Approach for Online Denoising

Abstract

This paper discusses the shortcomings of conventional denoising methods, and introduces a new approach for online denoising. The low-pass and average filtering methods, the most favored and widespread online denoising techniques, have their inherent limitations caused by operational characteristics. These methods can eliminate the noise of high-frequency, but cannot reduce the noise of low-frequency without loss of the information of interest signal. To solve the problem of the simple denoising methods, wavelet analysis has been applied for the denoising, and the wavelet filter has significantly increased the performance of denoising because of some advantages over Fourier analysis. The wavelet filter, however, is unsuitable for online processing, which is the main drawback of wavelet analysis. To overcome the problems of the conventional denoising methods, this paper suggests a new approach which guarantees high performance denoising and can be easily actualized via online processing. The main concept of the suggested denoising method is to estimate the long-term signal of interest using a single long-term moving window and multiple short-term moving windows. The denoising method based on the estimation of long-term signal can reduce the noise of all frequencies without the loss of the information of interest signal. In addition, the suggested denoising method, which uses the moving windows, is highly suitable for online signal processing. The efficient performance of this suggested denoising method is confirmed by some experiments.