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Hierarchical adaptive filtering
In the previous algorithm we do not use the hierarchy of structures.
We have explored many approaches for introducing a nonlinear
hierarchical law in the adaptive filtering and we found that the best
way was to link the threshold to the wavelet coefficient of the
previous plane w_{h}. We get:
and L is a threshold estimated by:
where S_{h} is the standard deviation of w_{h}. The function t(a)must return a value between 0 and 1. A possible function for t is:
 t(a) = 0 if

if a < k
Petra Nass
19990615