Wavelet Denoising Based on Genetic Algorithm

Abstract

This study is about using the genetic algorithm (GA) with wavelet transform (WT) for signal denoising purposes. The WT is a time-frequency signal analysis, and the GA is an optimization technique based on survival of the best solution using the maximized or minimized fitness value obtained from the fitness function. In this study, the parameters of WT are used as inputs for the GA for denoising the input signal that is corrupted by white Gaussian noise and gives an output of MSEo as fitness value. The input corrupted signal will pass through decomposition process to extract approximation and details coefficients, then thresholding the details coefficients using a threshold value in order to remove the noise, and finally reconstruction of the signal using the approximation and denoised details coefficients. Four standard benchmark signals are used to test this technique then a comparison is done with other studies in the same field, and the comparison showed that the results of this work is better.