COMPARATIVE STUDY OF WHITE GAUSSIAN NOISE REDUCTION FOR DIFFERENT SIGNALS USING WAVELET

Parul Saxena; Vinay Saxena

doi:10.29121/granthaalayah.v10.i7.2022.4711

Authors

Parul Saxena Assistant Professor, Department of Computer Science, Soban Singh Jeena University, Almora, 263601, Uttarakhand, India
Vinay Saxena Professor, Department of Mathematics, Kisan Post Graduate College Bahraich, 271801, Uttar Pradesh, India

DOI:

https://doi.org/10.29121/granthaalayah.v10.i7.2022.4711

Keywords:

Signal, White Gaussian Noise, Wavelet, Absolute Error, Mean Squared Error, Signal to Noise Ratio, Peak Signal to Noise Ratio

Abstract [English]

The present work is an attempt to make a comparative study of the wavelet-based noise reduction algorithm. The algorithm has been developed and implemented in MATLAB GUI and then analyzed for different types of wavelets such as Coiflet, Daubechies, Symlet, and Biorthogonal with their different versions. This algorithm has been verified for different types of input signals from different domains. Various statistical aspects like Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) are analyzed for this algorithm. It is observed that the developed algorithm for noise reduction using wavelet works very well for different types of wavelets.

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