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In the above process, we do not use the information between the wavelet coefficients at different scales. We modify the previous algorithm by introducing a prediction of the wavelet coefficient from the upper scale. This prediction could be determined from the regression [2] between the two scales but better results are obtained when we only set to . Between the expectation coefficient and the prediction, a dispersion exists where we assume that it is a Gaussian distribution:
The relation which gives the coefficient knowing and is:
with:
and:
This follows a Gaussian distribution with a mathematical expectation:
with:
is the barycentre of the three values , , 0 with the weights , , . The particular cases are:
At each scale, by changing all the wavelet coefficients of the plane by the estimate value , we get a Hierarchical Wiener Filter. The algorithm is: