From the course: Wavelet Analysis: Concepts with Wolfram Language
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Wavelet thresholding - Wolfram Language Tutorial
From the course: Wavelet Analysis: Concepts with Wolfram Language
Wavelet thresholding
Let's start with the basic example, wherein we are going to generate a noisy data and do some analysis on that. So this is the noisy data that we are looking at. It's basically a sine function with certain amount of noise on it. We perform a stationary wavelet transform on this. Stationary wavelet transform is something we haven't covered right now, but we will be covering in the next few slides. And now we are going to apply a certain wavelet threshold. The wavelet threshold automatically computes a certain threshold for you using a certain method, and then based on that, figures out which coefficients to keep. You can also specify yourself which method and which option you should use in order to compute the wavelet threshold. So in this case we are using a soft thresholding using the SURE algorithm. And when you perform the inverse wavelet transform and compare the result of the noisy signal that you had with the thresholded transform, you can see that this is the following result…
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