Hierarchical adaptive filtering

 In the previous algorithm we do not use the hierarchy of structures. We have explored many approaches for introducing a non-linear 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