Gaussian White Noise Removal Using Wavelet Modulus Maximum Algorithm

This paper presents a novel method for processing Gaussian white noise based on the wavelet modulus maximum algorithm, which effectively removes noise and enhances signal quality. We begin with a detailed explanation of the wavelet modulus maximum algorithm, highlighting its implementation approach that involves detecting local maxima in wavelet coefficients to distinguish signal features from noise. The algorithm typically utilizes thresholding techniques on modulus maxima across multiple decomposition levels to preserve important signal characteristics while suppressing noise components. Through concrete examples, we demonstrate its practical application in signal processing scenarios. In our experimental validation, we tested the method using multiple datasets and compared its performance against traditional noise removal techniques. The implementation involves wavelet decomposition using functions like wavedec(), modulus calculation, and iterative threshold adjustment across scales. Results indicate that this method significantly improves the signal-to-noise ratio of noisy signals, thereby effectively enhancing signal quality. Consequently, this approach holds broad application potential across various domains including speech processing, image denoising, and wireless communications, where it can be integrated into processing pipelines using wavelet toolbox functions or custom MATLAB/Python implementations.