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Compared to gaussian pyramid and laplacian pyramid, what are the advantages and disadvantages of wavelet decomposition for multiresolution image analysis? Thank you.

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  • $\begingroup$ Hi! This is sadly too broad. It's not clear what you've researched – and since all three terms are well-documented in literature and the internet, we must assume that you've at least tried to understand what they do, and hence, a bit on how they differ. That, however, enables you to ask a more specific question! So, please ask a specific question. $\endgroup$ – Marcus Müller Nov 23 '18 at 19:23
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Given the concise aspect of your question at the present time, a short answer is: wavelets are able to provide a critically-decimated representation (or non-redundant) of images, even orthogonal or close to. This is beneficial for applications such as compression (criticality), and for theoretical derivations (orthogonality). This can be a drawback for other practical uses.

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  • $\begingroup$ That's right. I'm working on image segmentation and I need a scale space such that details are neglected. Wavelets are less flexible than Gaussian pyramid in this case. $\endgroup$ – Kaiwen Chang Nov 27 '18 at 14:11
  • $\begingroup$ Steerable pyramids could be a useful addition $\endgroup$ – Laurent Duval Nov 27 '18 at 14:16

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