The objective of multiscale analysis is to study signal (time-scale) or image (scale-space) singularities at different scales or resolutions [1].

A powerful tool is the Wavelet Transform that works as derivative operator at different scales [2].
It provides a compact representation of the original signal and allows to also exploit inter and intra scale redundancy of its coefficients.
It is well-known its *persinstency property*: "large/small values of wavelet coefficients tend to propagate across scales" [3].
It can be easily seen in the following images where there are the original image (Lena) and its wavelet coefficients.

An interesting problem is to link wavelet coefficients at a given (intra) scale and/or along (inter) scales, since many signal processing problems (compression, denoising, retrieval etc.) strongly depend on it.

- A. Rosenfeld, M. Thurston, Edge and curve detection for visual scene analysis, IEEE Transactions on Comput., Vol. C-20, pp. 562-569, May 1971.
- S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, 1998.
- M. S. Crouse, R. D. Nowak, R. G. Baraniuk, Wavelet-based Statistical Signal Processing using Hidden Markov Models, IEEE Transactions on Signal Processing, Vol. 46, No. 4, pp. 886-902, April 1998.