Fuzzy Logic Based Thresholding for Hyper Shrinkage

Signal denoising is the process of reducing the unwanted noise in order to restore the original signal. Donoho and Johnstone’s denoising algorithm based on wavelet thresholding replace the small coefficients by zero and keep or shrink the coefficients with absolute value above the threshold. So the threshold selection becomes more important in signal denoising. In this paper the threshold selection based on Fuzzy Logic concepts for Hyper Shrinkage is developed. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. A fuzzy membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1. Wavelet Transform (WT) is useful for analyzing the non-stationary signal. The ElectroCardioGram (ECG) signal contains important information about the heart and here ECG signal is used to verify the proposed method. The Discrete Wavelet Transform (DWT) provides sufficient information both for analysis and synthesis of the original signal, with a significant reduction in the computation time. The Inverse Discrete Wavelet Transform (IDWT) provides the reconstruction of signals. The software used for the simulation is MATLAB.