PEREDUKSIAN ADDITIVE WHITE GAUSSIAN NOISE (AWGN) PADA SINYAL DATA MENGGUNAKAN DENOISING KOEFISIEN DARI TRANSFORMASI WAVELET

Authors

  • Riko Arlando Saragih
  • Aulia Oktafiandi

DOI:

https://doi.org/10.61769/telematika.v5i1.34

Abstract

Noise presence in real world data signal is inevitable.
Under ideal conditions, this noise may decrease to such negligible
levels so data obtained might be considered not corrupted by noise.
In denoising, wavelet attempts to remove the noise present in the
signal while preserving the signal characteristics. It involves three
steps, namely forward wavelet transform, thresholding step, and
inverse wavelet transform.
Based on simulations by using Hard Thresholding and SureShrink
with Empirical Wiener Filter, it was shown that Empirical Wiener
Filter using Hard Thresholded outperforms the other simulated
methods.

References

Bakhtazad, A., A., Palazoglu, dan J. A., Romagnoli, Process Data

Denoising Using Wavelet Transform, Intelligent Data Analysis 3 pages

-285, 1999.

Donoho, D. L., dan Iain M., Johnstone, Adapting to unknown

smoothness via wavelet shrinkage, Journal of the American Statistical

Association, 90(432):1200–1224, 1995.

Taswell, Carl, The What, How, and Why of Wavelet Shrinkage

Denoising, IEEE Computing in Science and Engineering, 2(3):12-19,

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Published

2017-10-03

Issue

Vol. 5 No. 1 (2009)

Section

Articles

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