Paper Title
Analysis Of Signal Denoising Methods Based On Wavelet Transform

The real world signals do not exist without noise. Wavelet Transform based denoising is a powerful method for suppressing noise in signals. In this paper, signal denoising based on Double-Density Discrete Wavelet Transform (DDDWT) and Dual-Tree Discrete Wavelet Transform (DTDWT) methods are implemented with optimum values of threshold point and level of decomposition. Based on the intensity of noise in the received signal, optimum values of threshold point and level of decomposition are determined. The results in terms of Root Mean Square Error (RMSE) and Signal to Noise Ratio (SNR) are then compared with the corresponding values of Discrete Wavelet Transform (DWT) based denoising method. The popular test signal; piece-regular contaminated with Additive White Gaussian Noise (AWGN) is chosen for the implementation. The results of MATLAB simulations show that for the selected threshold point and level of decomposition, the DDDWT and DTDWT perform better than the DWT method. Keywords: Signal Denoising, Discrete Wavelet Transform, Double- Density Discrete Wavelet Transform, Dual-Tree Discrete Wavelet Transform, Root mean square error.