Wavelet Scattering is an equivalent deep convolutional network, formed by cascade of wavelets, modulus nonlinearities, and lowpass filters. It yields representations that are time-shift invariant, robust to noise, and stable against time-warping deformations - proving useful in many classification tasks and attaining SOTA on limited datasets. Core results and intuition are provided in this ...
PyWavelets Breakdown: Wavelet, prior to integration, matches exactly with the shown code blob, which is an approximation of the complete real Morlet (used by Naive) assuming $\sigma > 5$ in the Wiki. pywt integrates real Morlet via np.cumsum(psi) * step, accounting for the differential step size The integrated wavelet, int_psi, is reused for all scales For each scale, the same int_psi is ...
What is the difference between soft thresholding and hard thresholding. Where we use soft and hard thresholding in image for denoising. I understand that in hard thresholding, the coefficients below
Thanks for pointing that out, there was indeed a mistake in my calculation of the 1D case (I edited the question to correct this). But the second one still doesn't normalize to 1 - I verified with Wolfram Alpha (via integration in polar coordinates).
I'm trying to looking the meaning and functionality about scaling function and wavelet function at wavelet analysis. I have googling already. But I can't find and understand the meaning. What does
How can power or energy be computed from Continuous Wavelet Transform? Is it just $\sum |\text {CWT} (x)|^2$, or are there other considerations, particularly if interested in a subset of frequencies?...
Low scales are arguably the most challenging to implement due to limitations in discretized representations. Detailed comparison here; the principal difference is in how the two handle wavelets at ...
Discrete wavelet transform; how to interpret approximation and detail coefficients? Ask Question Asked 8 years, 2 months ago Modified 2 years, 10 months ago
I am using the Synchrosqueezing Wavelet Transform and I want to compare it to classical CWT. For this, I use a signal consisting of a chirp. Strangely, in the SST result, it looks like the chirp ha...
Interpretation of wavelet trasformation (synchrosqueezing) I'm working on a dataset of spectroscopies and i'm classifying them by transforming the 1D signal into a 2D one by the ssqueezepy library. For instance, consider to ...
