Smoothing non-equidistantly spaced data using second generation wavelets and thresholding
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A classical (first generation) wavelet transform assumes the input to be a regularly sampled signal. In most applications of digital signal processing or digital image processing, this assumption corresponds to reality. In many other applications however, data are not available on a regular grid, but rather as non-equidistant samples. Examples in this chapter illustrate what happens if we use classical wavelet transforms, pretending that the data are equispaced: the irregularity of the grid is reflected in the output.