Relationship between number of wavelet decomposition level and noise level

1. How do you choose a wavelet decomposition level?
2. What is the significance of wavelet transformations can it be used in noise reduction?
3. How wavelet transform can be used for signal denoising?
4. What do you understand by wavelet decomposition technique for measurement of surfaces?

How do you choose a wavelet decomposition level?

Theoretically, the maximum decomposition level (M) can be calculated as: M = log2 (N), where N is the series length. When conducting a wavelet-based ANN model, it needs to determine the most suitable decomposition level from 1 to M.

What is the significance of wavelet transformations can it be used in noise reduction?

Wavelet Transform (WT) is a powerful tool for removal of noise from various signals. Combining WT with other noise reducing techniques may result in further reduction of noise. Similar to WT, Singular Vector Decomposition (SVD) is also an effective noise reduction tool.

How wavelet transform can be used for signal denoising?

In order to de-noise any signal, we need to put the noisy signal into the decomposition process by applying wavelet transform. Wavelet transform allows us to decompose signal into groups of coefficients at different frequency levels.

What do you understand by wavelet decomposition technique for measurement of surfaces?

The wavelet transform is a mathematical technique which can decompose a signal into multiple lower resolution levels by controlling the scaling and shifting factors of a single wavelet function (mother wavelet) (Foufoula-Georgiou and Kumar, 1995; Lau and Weng, 1995; Torrence and Compo, 1998; Percival and Walden, 2000).