PEREDUKSIAN ADDITIVE WHITE GAUSSIAN NOISE (AWGN) PADA SINYAL DATA MENGGUNAKAN DENOISING KOEFISIEN DARI TRANSFORMASI WAVELET - Saragih, Riko Arlando; Oktafiandi, Aulia (Jurnal Telematika, 2009)
Noise presence in real world data signal is inevitable.Under ideal conditions, this noise may decrease to such negligiblelevels so data obtained might be considered not corrupted by noise.In denoising, wavelet attempts to remove the noise present in thesignal while preserving the signal characteristics. It involves threesteps, namely forward wavelet transform, thresholding step, andinverse wavelet transform.Based on simulations by using Hard Thresholding and SureShrinkwith Empirical Wiener Filter, it was shown that Empirical WienerFilter using Hard Thresholded outperforms the other simulatedmethods.