A Fast Signal Denoising Algorithm Based on the LPA-ICI Method for Real-Time Applications

Abstract

We propose a new algorithm for denoising of additive white Gaussian noise-corrupted signals, based on the intersection of confidence intervals (ICI) algorithm, called the fast intersection of confidence intervals (FICI) algorithm. The proposed approach combines the FICI algorithm, used for the adaptive filter support size selection, with the local polynomial approximation (LPA) method, used as a filter design tool. The LPA-FICI method, when compared to the existing ICI-based denoising method, reduces the computational complexity by up to N times, where N is the number of signal samples, resulting in significantly faster algorithm execution time, while maintaining the estimation accuracy close to the one achieved using the original ICI-based method. Furthermore, the proposed modifications allow the use of the LPA-FICI method in real-time signal processing. In conducted simulations, we have confirmed advantages of the proposed method on two commonly used benchmark signals corrupted with various noise strengths.