Why we have to do reconstruction after decomposition in wavelet transform?
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The main advantage of wavelet basis is that they despite having irregular shape are able to perfectly reconstruct functions with linear and higher order polynomial shapes, such as, rect, triangle, 2nd order polynomials, etc. Note that Fourier basis fail to do so, as in case of famous example of rect function at the edges. As a result, wavelets are able to denoise the particular signals far better than conventional filters that are based on Fourier transform design and that do not follow the algebraic rules obeyed by the wavelets.
You'll do a lot of injustice to wavelets if you treat them merely as filters. To appreciate their power I'd suggest to read the underlying theory used to develop wavelets.
MARK BRAINLIEST..
You'll do a lot of injustice to wavelets if you treat them merely as filters. To appreciate their power I'd suggest to read the underlying theory used to develop wavelets.
MARK BRAINLIEST..
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